02 feature_preprocessing - numeric.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"wObiuw_qeCNN"},"source":["<img src=\"https://github.com/AmsterdamUMC/AmsterdamUMCdb/blob/master/img/logo_c4i_square.png?raw=1\" alt=\"Logo C4I\" width=128px/>\n","\n","<img src=\"https://github.com/AmsterdamUMC/AmsterdamUMCdb/blob/master/img/logo_amds.png?raw=1\" alt=\"Logo AMDS\" width=128px/>\n","\n","# AmsterdamUMCdb with Google BigQuery and Colaboratory\n","## AmsterdamUMCdb - Freely Accessible ICU Database\n","## Task 1: Feature selection for septic patients\n","\n"]},{"cell_type":"markdown","metadata":{"id":"dnF5AaxGGhDq"},"source":["<a name=\"cell-project-id\"></a>\n","# Retrieving your Google Cloud Project ID\n","- At the top of the screen, select **My First Project** or [create a new one](https://console.cloud.google.com/projectcreate). Please write down the Project ID in the cell below (\"Insert text here\") and **Run** the cell to store the value."]},{"cell_type":"code","execution_count":1,"metadata":{"executionInfo":{"elapsed":2,"status":"ok","timestamp":1702229227612,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"hQnpY5Kpm5rT"},"outputs":[],"source":["# sets *your* project id\n","PROJECT_ID = \"premium-episode-405909\" #@param {type:\"string\"}"]},{"cell_type":"markdown","metadata":{"id":"Ybi3I6p9d0Kp"},"source":["# Sets the default BigQuery dataset for accessing AmsterdamUMCdb\n","\n","If you have received instructions to use a specific BigQuery instance, change the default settings here. Otherwise use these default values."]},{"cell_type":"code","execution_count":2,"metadata":{"executionInfo":{"elapsed":2,"status":"ok","timestamp":1702229227612,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"5lVy6FoueMZn"},"outputs":[],"source":["# sets default dataset for AmsterdamUMCdb\n","DATASET_PROJECT_ID = 'amsterdamumcdb' #@param {type:\"string\"}\n","DATASET_ID = 'version1_0_2' #@param {type:\"string\"}\n","LOCATION = 'eu' #@param {type:\"string\"}"]},{"cell_type":"markdown","metadata":{"id":"PB_NEmEZtLpg"},"source":["# Provide your credentials to access the AmsterdamUMCdb dataset on Google BigQuery\n","Authenticate your credentials with Google Cloud Platform and set your default Google Cloud Project ID as an environment variable for running query jobs.\n","\n","1. Run the cell. The `Allow this notebook to access your Google credentials?` prompt appears. Select `Allow`.\n","2. In the `Sign in - Google Accounts` dialog, use the account you registered during the AmsterdamUMCdb application process and select `Allow` again."]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":13292,"status":"ok","timestamp":1702229242823,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"v0L2i-Nnp7_R","outputId":"81c76685-dd75-4282-99ec-fa1cf2ec5853"},"outputs":[{"name":"stdout","output_type":"stream","text":["Authenticated\n"]}],"source":["import os\n","from google.colab import auth\n","\n","# all libraries check this environment variable, so set it:\n","os.environ[\"GOOGLE_CLOUD_PROJECT\"] = PROJECT_ID\n","\n","auth.authenticate_user()\n","print('Authenticated')"]},{"cell_type":"markdown","metadata":{"id":"1m9L8PWsGqjB"},"source":["# Enable data table display\n","\n","Colab includes the `google.colab.data_table` package that can be used to display Pandas dataframes as an interactive data table (default limits: `max_rows = 20000`, `max_columns = 20`). This is especially useful when exploring the  tables or dictionary from AmsterdamUMCdb. It can be enabled with:"]},{"cell_type":"code","execution_count":4,"metadata":{"executionInfo":{"elapsed":2,"status":"ok","timestamp":1702229242824,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"LMNA-vBHPyHz"},"outputs":[],"source":["%load_ext google.colab.data_table\n","from google.colab.data_table import DataTable\n","\n","# change default limits:\n","DataTable.max_columns = 50\n","DataTable.max_rows = 30000"]},{"cell_type":"markdown","metadata":{"id":"Bpsujp7-GxTZ"},"source":["\n","## Set the default query job configuration for magics"]},{"cell_type":"code","execution_count":5,"metadata":{"executionInfo":{"elapsed":2589,"status":"ok","timestamp":1702229248469,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"MC7nOYOCKFvQ"},"outputs":[],"source":["from google.cloud.bigquery import magics\n","from google.cloud import bigquery\n","\n","# sets the default query job configuration\n","def_config = bigquery.job.QueryJobConfig(default_dataset=DATASET_PROJECT_ID + \".\" + DATASET_ID)\n","magics.context.default_query_job_config = def_config"]},{"cell_type":"code","execution_count":6,"metadata":{"executionInfo":{"elapsed":2,"status":"ok","timestamp":1702229248469,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"ywZLTXha9CE8"},"outputs":[],"source":["import pandas as pd\n","config_gbq = {'query':\n","          {'defaultDataset': {\n","              \"datasetId\": DATASET_ID,\n","              \"projectId\": DATASET_PROJECT_ID\n","              },\n","           'Location': LOCATION}\n","           }"]},{"cell_type":"markdown","metadata":{"id":"H9wwX7aO0ROL"},"source":["## Query the admission table and copy the data to the `admissions` Pandas dataframe:\n","\n","The admissions table contains the ICU admissions of all patients in AmsterdamUMCdb. More information can be found here: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/AmsterdamUMC/AmsterdamUMCdb/blob/master/tables/admissions.ipynb)\n","\n","Since this is a relatively small table, it is acceptable to use `SELECT *`.\n","\n","**Note**: Should an error occur while running the query, please see\n","the AmsterdamUMCdb BigQuery [Frequently Asked Questions](https://github.com/AmsterdamUMC/AmsterdamUMCdb/wiki/bigquery#faq)."]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["0848aa0192fc476f91ba0b7cc973bd3b","1744ddd8ddcc4d0fb5d4fc053a2cbe8c","3783d8fd9f3a475b96a4001d0e967d1d","01584c950a6d4565a9779b2ab589fd9c","d60f1bc0629a4169818af6c089617757","d682da962bfa4d60bf7281388d734376","0adc62eb122f40a8b6cb7768b22287ea","812a97cdfa374d42b6e6843a5061c8c6","ff7ac5d9b0914b2bbe7288dbfcc854a6","7661966ba7e046488ce46c3c0e94ae21","1801f01fedd14b328304870794a7a1d2","c3354f29776648cc8088a5691d2e1994","75d7453c78e34eee8483606f62c7c273","30c14a6831b94506af203a776e44f112","88268e58dba94891981edae10bd635f6","3bf653668c614162a2bc910f4235979c","4df8424385d3415f98d9b19f818d3e07","282ef83eb8654c91b790ca5afb2d92f0","430759dea2c24d88b07f36138a1ab619","17cc6df680064ba39379a11070985546","21885f57ccc2471780c6ad404424a189","9d71e3d825964208ab219e84819a3683"]},"executionInfo":{"elapsed":2876,"status":"ok","timestamp":1702229251344,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"cLh05lDpJ4wj","outputId":"08ae9443-fe0c-4376-a091-1f93725854bc"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"0848aa0192fc476f91ba0b7cc973bd3b","version_major":2,"version_minor":0},"text/plain":["Query is running:   0%|          |"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"c3354f29776648cc8088a5691d2e1994","version_major":2,"version_minor":0},"text/plain":["Downloading:   0%|          |"]},"metadata":{},"output_type":"display_data"}],"source":["%%bigquery admissions\n","SELECT * FROM admissions"]},{"cell_type":"markdown","metadata":{"id":"zbvWkl5p0ERl"},"source":["## Display the `admissions` dataframe."]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":967,"output_embedded_package_id":"1h1gib6dNoF1Kzpgow-KdjxxVNeKEMm7m"},"executionInfo":{"elapsed":13642,"status":"ok","timestamp":1701861849908,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"xgopBClCKs_0","outputId":"618692a1-4e64-4a47-ad67-4d86a6e165cd"},"outputs":[{"data":{"text/plain":["Output hidden; open in https://colab.research.google.com to view."]},"metadata":{},"output_type":"display_data"}],"source":["admissions"]},{"cell_type":"markdown","metadata":{"id":"aa5jI637xCvH"},"source":["# Getting the data you need\n","The `amsterdamumcdb` package provides the `get_dictionary()` function that returns a DataFrame containing all `item`s and `itemid`s in AmsterdamUMCdb. In combination with BigQuery DataTables, it's possible to quickly locate an item of interest. Since AmsterdamUMCdb originated from a real Dutch ICU database, the original item names are in Dutch. For common ICU parameters, English translations have been provided as `item_en` and `category_en`. In addition, as part of the `ICUnity` project in preparation for the [The Dutch ICU Data Warehouse](https://www.icudata.nl/index-en.html) a number of commonly used items have been mapped to [LOINC](https://decor.nictiz.nl/art-decor/loinc), [SNOMED CT](https://decor.nictiz.nl/art-decor/snomed-ct) or [ATC](https://www.whocc.no/atc_ddd_index/) with data in the `vocabulary_id`, `vocabulary_concept_code`, `vocabulary_concept_name` columns. The mapping process is still very much work in progress, but we are happy to take [pull requests](https://github.com/AmsterdamUMC/AmsterdamUMCdb/pulls) for the [dictionary.csv](https://github.com/AmsterdamUMC/AmsterdamUMCdb/blob/master/amsterdamumcdb/dictionary/dictionary.csv) file, if you have any additions."]},{"cell_type":"markdown","metadata":{"id":"riKnnGLe0ynk"},"source":["## Install the AmsterdamUMCdb package"]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9309,"status":"ok","timestamp":1702229262975,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"kefpJwsNZ0sf","outputId":"4356c7c8-e7d6-45b6-f07c-99f1d399db19"},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/273.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m \u001b[32m204.8/273.5 kB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m273.5/273.5 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h"]}],"source":["# get the amsterdamumcdb package from PyPI repository for use in Colab\n","!pip install amsterdamumcdb --quiet\n","import amsterdamumcdb as adb"]},{"cell_type":"markdown","metadata":{"id":"csx_BSWT1Uil"},"source":["## Load the AmsterdamUMCdb dictionary.\n","Use the **Filter** button to quickly browse through the dictionary."]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":2752,"output_embedded_package_id":"1yswqhYHLAomuldNLRgKTOsNqgYMd7fPH"},"executionInfo":{"elapsed":9650,"status":"ok","timestamp":1701861869319,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"KVbGQ9ubvf_R","outputId":"183c7135-3508-4851-d23f-f431f2795f3c"},"outputs":[{"data":{"text/plain":["Output hidden; open in https://colab.research.google.com to view."]},"metadata":{},"output_type":"display_data"}],"source":["dictionary = adb.get_dictionary()\n","dictionary"]},{"cell_type":"code","execution_count":21,"metadata":{"executionInfo":{"elapsed":2,"status":"ok","timestamp":1701863299594,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"omrp0Qbp4GZB"},"outputs":[],"source":["list_of_items_en = ['heart rate', 'non-invasive mean arterial pressure','oxygen saturation SpO2','hematocrit',\n","       'hemoglobin', 'white blood cell count', 'platelets','bilirubin total','serum creatinine',\n","       'lactate','respiratory rate spontaneous', 'sodium', 'potassium','skin temperature']"]},{"cell_type":"code","execution_count":18,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":397,"status":"ok","timestamp":1701862988552,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"140gH3rj79bH","outputId":"e2970fa3-7a51-4da4-b596-a0ac945b3199"},"outputs":[{"data":{"text/plain":["84      Actief openen van de ogen\n","85      Actief openen van de ogen\n","86      Actief openen van de ogen\n","87      Actief openen van de ogen\n","3596                        A_Eye\n","3597                        A_Eye\n","3598                        A_Eye\n","3599                        A_Eye\n","4133                       RA_Eye\n","4134                       RA_Eye\n","4135                       RA_Eye\n","5415                      MCA_Eye\n","5416                      MCA_Eye\n","5417                      MCA_Eye\n","5418                      MCA_Eye\n","8348             E_EMV_NICE_24uur\n","8349             E_EMV_NICE_24uur\n","8350             E_EMV_NICE_24uur\n","8351             E_EMV_NICE_24uur\n","8364            E_EMV_NICE_Opname\n","8365            E_EMV_NICE_Opname\n","8366            E_EMV_NICE_Opname\n","8367            E_EMV_NICE_Opname\n","Name: item, dtype: object"]},"execution_count":18,"metadata":{},"output_type":"execute_result"}],"source":["dictionary[dictionary['item_en']=='Glasgow Coma Score eye opening subscore']['item']"]},{"cell_type":"code","execution_count":22,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":550,"status":"ok","timestamp":1701863303258,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"yrbMQtqN4VZY","outputId":"b3824d5a-4660-44b1-a963-1aed4ba5c44a"},"outputs":[{"data":{"text/plain":["['Hartfrequentie',\n"," 'Niet invasieve bloeddruk gemiddeld',\n"," 'Saturatie (Monitor)',\n"," 'Hematocriet',\n"," 'Ht (bloed)',\n"," 'Ht(v.Bgs) (bloed)',\n"," 'Hemoglobine',\n"," 'Hb (bloed)',\n"," 'Leucocyten',\n"," \"Leuco's (bloed)\",\n"," 'Thrombocyten',\n"," \"Thrombo's (bloed)\",\n"," 'Bili Totaal',\n"," 'Bilirubine (bloed)',\n"," 'Kreatinine',\n"," 'Kreatinine (bloed)',\n"," 'KREAT enzym. (bloed)',\n"," 'Laktaat',\n"," 'Lactaat (bloed)',\n"," 'Ademfrequentie Spontaan',\n"," 'Frequentie Spontaan',\n"," 'Ademfrequentie Spontaan(2)',\n"," 'Ademfreq. Spontaan nieuw',\n"," 'Natrium (bloed)',\n"," 'Kalium (bloed)',\n"," 'Temp Huid']"]},"execution_count":22,"metadata":{},"output_type":"execute_result"}],"source":["list_of_items = []\n","for item in list_of_items_en:\n","  temp_item = dictionary[dictionary['item_en']==item]['item']\n","  for item2 in temp_item:\n","    if item2 not in list_of_items:\n","      list_of_items.append(item2)\n","list_of_items"]},{"cell_type":"code","execution_count":23,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":305,"status":"ok","timestamp":1701863307283,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"MxIF_0OU8dDV","outputId":"2339dc2c-ab70-4687-860f-68fa3cc4672c"},"outputs":[{"data":{"text/plain":["26"]},"execution_count":23,"metadata":{},"output_type":"execute_result"}],"source":["len(list_of_items)"]},{"cell_type":"markdown","metadata":{"id":"vK3dtu4eBtt4"},"source":["# Selecting relevant features\n"]},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1710,"output_embedded_package_id":"1XG0IYMAVZhHIm6tZUxHP7XvJABwkLYbg"},"executionInfo":{"elapsed":43598,"status":"ok","timestamp":1702229307446,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"Vy41_CqM8Bfh","outputId":"a3db8e5a-ef30-46e7-ee47-f2060ab91a0f"},"outputs":[{"data":{"text/plain":["Output hidden; open in https://colab.research.google.com to view."]},"metadata":{},"output_type":"display_data"}],"source":["septic_patients = adb.cohorts.get_sepsis_patients(config_gbq)\n","septic_patients"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":205,"status":"ok","timestamp":1701077605623,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"4oIfZgJo-p9K","outputId":"de6afb38-8a24-40a1-f658-75ac612e151c"},"outputs":[{"data":{"text/plain":["Index(['patientid', 'admissionid', 'admissioncount', 'location', 'urgency',\n","       'origin', 'admittedat', 'admissionyeargroup', 'dischargedat',\n","       'lengthofstay', 'destination', 'gender', 'agegroup', 'dateofdeath',\n","       'weightgroup', 'weightsource', 'heightgroup', 'heightsource',\n","       'specialty', 'diagnosis_type', 'diagnosis', 'diagnosis_id',\n","       'diagnosis_subgroup', 'diagnosis_subgroup_id', 'diagnosis_group',\n","       'diagnosis_group_id', 'surgical', 'sepsis_at_admission',\n","       'sepsis_antibiotics_bool', 'sepsis_antibiotics_given',\n","       'other_antibiotics_bool', 'other_antibiotics_given',\n","       'sepsis_cultures_bool', 'sepsis_cultures_drawn',\n","       'sofa_respiration_score', 'sofa_coagulation_score', 'sofa_liver_score',\n","       'sofa_cardiovascular_score', 'sofa_cns_score', 'sofa_renal_score',\n","       'sofa_total_score'],\n","      dtype='object')"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["septic_patients.columns"]},{"cell_type":"code","execution_count":10,"metadata":{"executionInfo":{"elapsed":6,"status":"ok","timestamp":1702229307446,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"HsB_YYdjrLcm"},"outputs":[],"source":["list_admission_ids = [str(id) for id in septic_patients[\"admissionid\"].unique().tolist()]\n","\n","query_config = {\n","    'query': {\n","        'parameterMode': 'NAMED',\n","        'queryParameters': [\n","            {\n","                'name': 'list_admission_ids',\n","                'parameterType': {'type': 'ARRAY',\n","                                  'arrayType': {'type': 'STRING'}},\n","                'parameterValue': {'arrayValues': [{'value': i} for i in list_admission_ids]}\n","            },\n","        ],\n","        'defaultDataset': {\n","              \"datasetId\": DATASET_ID,\n","              \"projectId\": DATASET_PROJECT_ID\n","              },\n","           'Location': LOCATION\n","    }\n","}"]},{"cell_type":"code","execution_count":11,"metadata":{"executionInfo":{"elapsed":6,"status":"ok","timestamp":1702229307446,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"dBgqvmUzQoMb"},"outputs":[],"source":["list_admission_ids = septic_patients[septic_patients['lengthofstay']<=720]['admissionid'].unique().tolist()"]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1702229307446,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"kqAKvyFX4567","outputId":"70447c91-9e93-44ba-875d-8e949e5fbe0d"},"outputs":[{"data":{"text/plain":["2810"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["len(list_admission_ids) # 2810"]},{"cell_type":"code","execution_count":30,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"executionInfo":{"elapsed":733,"status":"ok","timestamp":1701952775201,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"XDGrGXhCOzIB","outputId":"9beadbcb-999f-4744-e605-59c13042bcbb"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# destination of septic patients\n","import matplotlib.pyplot as plt\n","\n","# Your list of data\n","hist_data = septic_patients[septic_patients['lengthofstay']<=1000]['lengthofstay'] #[septic_patients['destination']=='Overleden']\n","\n","# Plotting the histogram\n","plt.hist(hist_data, bins=5, edgecolor='black')\n","plt.title('Histogram of Data')\n","plt.xlabel('Values')\n","plt.ylabel('Frequency')\n","\n","# Show the plot\n","plt.show()"]},{"cell_type":"code","execution_count":46,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":669,"output_embedded_package_id":"15Hl5DVPXulwBXEmrWVUQnVPeAWtNopoj"},"executionInfo":{"elapsed":14835,"status":"ok","timestamp":1701956131927,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"Se_nei4bE7zq","outputId":"954846f1-7e19-4351-f31c-0fa32f7e71c7"},"outputs":[{"data":{"text/plain":["Output hidden; open in https://colab.research.google.com to view."]},"metadata":{},"output_type":"display_data"}],"source":["# Steroids drugs\n","drugs_table = pd.read_gbq(\n","    '''\n","    SELECT admissionid, start AS start_drug, stop AS stop_drug, duration AS duration_drug, action AS action_drug, dose AS dose_drug, fluidin AS fluid_in_drug\n","    FROM drugitems\n","    WHERE (LOWER(item) LIKE '%steroid%'\n","      OR LOWER(item) LIKE '%corti%'\n","      OR LOWER(ordercategory) LIKE '%steroid%'\n","      OR LOWER(ordercategory) LIKE '%corti%')\n","      AND (LOWER(item) NOT LIKE '%acetaat%')\n","      AND (LOWER(ordercategory) NOT LIKE '%acetaat%')\n","      AND CAST(admissionid AS STRING) IN UNNEST(@list_admission_ids)\n","    '''\n","    , configuration=query_config, use_bqstorage_api=True).sort_values(\"admissionid\")\n","drugs_table"]},{"cell_type":"code","execution_count":47,"metadata":{"executionInfo":{"elapsed":3,"status":"ok","timestamp":1701956131927,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"U2ZKbuahhTiZ"},"outputs":[],"source":["int_admission_ids = [int(number) for number in list_admission_ids]"]},{"cell_type":"code","execution_count":13,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":566},"executionInfo":{"elapsed":34924,"status":"ok","timestamp":1702229342366,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"uANFynQMPn4p","outputId":"d1a2bb0d-fb08-4116-8280-fb4ae61f500c"},"outputs":[{"name":"stdout","output_type":"stream","text":["Warning: total number of rows (19920937) exceeds max_rows (30000). Falling back to pandas display.\n"]},{"data":{"text/html":["\n","  <div id=\"df-0c5f7039-37de-4d42-a732-92d57d8535c2\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>admissionid</th>\n","      <th>value_numericitem</th>\n","      <th>item_numericitem</th>\n","      <th>unit_numericitem</th>\n","      <th>measuredat_numericitem</th>\n","      <th>updatedat_numericitem</th>\n","      <th>fluidout_numericitem</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>6748486</th>\n","      <td>11</td>\n","      <td>-100.00</td>\n","      <td>PD2 min ingesteld</td>\n","      <td>mmHg</td>\n","      <td>54240000</td>\n","      <td>54660000</td>\n","      <td>&lt;NA&gt;</td>\n","    </tr>\n","    <tr>\n","      <th>19255925</th>\n","      <td>11</td>\n","      <td>7.80</td>\n","      <td>Hb (bloed)</td>\n","      <td>mmol/l</td>\n","      <td>-6540000</td>\n","      <td>139340</td>\n","      <td>&lt;NA&gt;</td>\n","    </tr>\n","    <tr>\n","      <th>593818</th>\n","      <td>11</td>\n","      <td>88.00</td>\n","      <td>Veneuze druk braun</td>\n","      <td>mmHg</td>\n","      <td>97440000</td>\n","      <td>102900000</td>\n","      <td>&lt;NA&gt;</td>\n","    </tr>\n","    <tr>\n","      <th>10550232</th>\n","      <td>11</td>\n","      <td>0.59</td>\n","      <td>Insp. Time (sec) nieuw</td>\n","      <td>sec</td>\n","      <td>39840000</td>\n","      <td>40800000</td>\n","      <td>&lt;NA&gt;</td>\n","    </tr>\n","    <tr>\n","      <th>10834308</th>\n","      <td>11</td>\n","      <td>200.00</td>\n","      <td>TMP max ingesteld</td>\n","      <td>mmHg</td>\n","      <td>79440000</td>\n","      <td>80760000</td>\n","      <td>&lt;NA&gt;</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>7859474</th>\n","      <td>23540</td>\n","      <td>75.00</td>\n","      <td>UrineCAD</td>\n","      <td>ml</td>\n","      <td>149820000</td>\n","      <td>150675677</td>\n","      <td>75</td>\n","    </tr>\n","    <tr>\n","      <th>9961841</th>\n","      <td>23540</td>\n","      <td>13.00</td>\n","      <td>SBI (Zwakke ademhalingsindex)</td>\n","      <td>Geen</td>\n","      <td>139020000</td>\n","      <td>142140000</td>\n","      <td>&lt;NA&gt;</td>\n","    </tr>\n","    <tr>\n","      <th>8864821</th>\n","      <td>23540</td>\n","      <td>11.00</td>\n","      <td>Mean luchtweg druk</td>\n","      <td>cmH2O</td>\n","      <td>121020000</td>\n","      <td>121620000</td>\n","      <td>&lt;NA&gt;</td>\n","    </tr>\n","    <tr>\n","      <th>11665739</th>\n","      <td>23540</td>\n","      <td>50.00</td>\n","      <td>MaagRetNietWeg</td>\n","      <td>ml</td>\n","      <td>128220000</td>\n","      <td>128893840</td>\n","      <td>&lt;NA&gt;</td>\n","    </tr>\n","    <tr>\n","      <th>7648180</th>\n","      <td>23540</td>\n","      <td>0.20</td>\n","      <td>Insp. Rise time (Set, sec) nieuw</td>\n","      <td>sec</td>\n","      <td>117420000</td>\n","      <td>120600000</td>\n","      <td>&lt;NA&gt;</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>19920937 rows × 7 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0c5f7039-37de-4d42-a732-92d57d8535c2')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-0c5f7039-37de-4d42-a732-92d57d8535c2 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Time (sec) nieuw   \n","10834308           11             200.00                 TMP max ingesteld   \n","...               ...                ...                               ...   \n","7859474         23540              75.00                          UrineCAD   \n","9961841         23540              13.00     SBI (Zwakke ademhalingsindex)   \n","8864821         23540              11.00                Mean luchtweg druk   \n","11665739        23540              50.00                    MaagRetNietWeg   \n","7648180         23540               0.20  Insp. Rise time (Set, sec) nieuw   \n","\n","         unit_numericitem  measuredat_numericitem  updatedat_numericitem  \\\n","6748486              mmHg                54240000               54660000   \n","19255925           mmol/l                -6540000                 139340   \n","593818               mmHg                97440000              102900000   \n","10550232              sec                39840000               40800000   \n","10834308             mmHg                79440000               80760000   \n","...                   ...                     ...                    ...   \n","7859474                ml               149820000              150675677   \n","9961841              Geen               139020000              142140000   \n","8864821             cmH2O               121020000              121620000   \n","11665739               ml               128220000              128893840   \n","7648180               sec               117420000              120600000   \n","\n","          fluidout_numericitem  \n","6748486                   <NA>  \n","19255925                  <NA>  \n","593818                    <NA>  \n","10550232                  <NA>  \n","10834308                  <NA>  \n","...                        ...  \n","7859474                     75  \n","9961841                   <NA>  \n","8864821                   <NA>  \n","11665739                  <NA>  \n","7648180                   <NA>  \n","\n","[19920937 rows x 7 columns]"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["# steroids, sepsis lab results\n","numericitems_table = pd.read_gbq(\n","    '''\n","    SELECT admissionid, value AS value_numericitem, item AS item_numericitem, unit AS unit_numericitem, measuredat AS measuredat_numericitem, updatedat AS updatedat_numericitem, fluidout AS fluidout_numericitem\n","    FROM numericitems\n","    WHERE CAST(admissionid AS STRING) IN UNNEST(@list_admission_ids)\n","    '''\n","    , configuration=query_config, use_bqstorage_api=True).sort_values(\"admissionid\")\n","numericitems_table"]},{"cell_type":"code","execution_count":14,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":735},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1702229342366,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"Ut_o2BJcCQnK","outputId":"f8f467a7-2bb5-48e8-a7f7-b5ebce9f5fbe"},"outputs":[{"data":{"application/vnd.google.colaboratory.module+javascript":"\n      import \"https://ssl.gstatic.com/colaboratory/data_table/881c4a0d49046431/data_table.js\";\n\n      const table = window.createDataTable({\n        data: [[{\n            'v': 491254,\n            'f': \"491254\",\n        },\n{\n            'v': 99,\n            'f': \"99\",\n        },\n{\n            'v': 3.0599999,\n            'f': \"3.0599999\",\n        },\n\"Prothrombinetijd  (bloed)\",\n\"INR\",\n{\n            'v': 63537960000,\n            'f': \"63537960000\",\n        },\n{\n            'v': 64553427023,\n            'f': \"64553427023\",\n        },\n\"<NA>\"],\n [{\n            'v': 12921069,\n            'f': \"12921069\",\n        },\n{\n            'v': 99,\n            'f': \"99\",\n        },\n{\n            'v': 2.23,\n            'f': \"2.23\",\n        },\n\"Prothrombinetijd  (bloed)\",\n\"INR\",\n{\n            'v': 64137240000,\n            'f': \"64137240000\",\n        },\n{\n            'v': 64553435943,\n            'f': \"64553435943\",\n        },\n\"<NA>\"],\n [{\n            'v': 18242771,\n            'f': \"18242771\",\n        },\n{\n            'v': 99,\n            'f': \"99\",\n        },\n{\n            'v': 5.6999998,\n            'f': \"5.6999998\",\n        },\n\"Kalium (bloed)\",\n\"mmol/l\",\n{\n            'v': 64137540000,\n            'f': \"64137540000\",\n        },\n{\n            'v': 64553442396,\n            'f': \"64553442396\",\n        },\n\"<NA>\"],\n [{\n            'v': 2443985,\n            'f': \"2443985\",\n        },\n{\n            'v': 99,\n            'f': \"99\",\n        },\n{\n            'v': 623.0,\n            'f': \"623.0\",\n        },\n\"Kreatinine (bloed)\",\n\"\\u00b5mol/l\",\n{\n            'v': 64519920000,\n            'f': \"64519920000\",\n        },\n{\n            'v': 64553621723,\n            'f': \"64553621723\",\n        },\n\"<NA>\"],\n [{\n            'v': 18214944,\n            'f': \"18214944\",\n        },\n{\n            'v': 99,\n            'f': \"99\",\n        },\n{\n            'v': 336.0,\n            'f': \"336.0\",\n        },\n\"Thrombo's (bloed)\",\n\"10^9/l\",\n{\n            'v': 64519920000,\n            'f': \"64519920000\",\n        },\n{\n            'v': 64553620710,\n            'f': \"64553620710\",\n        },\n\"<NA>\"],\n [{\n            'v': 13755100,\n            'f': \"13755100\",\n        },\n{\n            'v': 99,\n            'f': \"99\",\n        },\n{\n            'v': 94.0,\n            'f': \"94.0\",\n        },\n\"Bezinking (bloed)\",\n\"mm/uur\",\n{\n            'v': 64519920000,\n            'f': \"64519920000\",\n        },\n{\n            'v': 64553544836,\n            'f': \"64553544836\",\n        },\n\"<NA>\"],\n [{\n            'v': 409878,\n            'f': \"409878\",\n        },\n{\n            'v': 99,\n            'f': \"99\",\n        },\n{\n            'v': 20.6,\n            'f': \"20.6\",\n        },\n\"Leuco's (bloed)\",\n\"10^9/l\",\n{\n            'v': 64519920000,\n            'f': \"64519920000\",\n        },\n{\n            'v': 64553620756,\n            'f': \"64553620756\",\n        },\n\"<NA>\"],\n [{\n            'v': 11823575,\n            'f': \"11823575\",\n        },\n{\n            'v': 99,\n            'f': \"99\",\n        },\n{\n            'v': 5.3000002,\n            'f': \"5.3000002\",\n        },\n\"Kalium (bloed)\",\n\"mmol/l\",\n{\n            'v': 64519920000,\n            'f': \"64519920000\",\n        },\n{\n            'v': 64553621583,\n            'f': \"64553621583\",\n        },\n\"<NA>\"],\n [{\n            'v': 3059487,\n            'f': \"3059487\",\n        },\n{\n            'v': 99,\n            'f': \"99\",\n        },\n{\n            'v': 140.0,\n            'f': \"140.0\",\n        },\n\"Alk.Fosf. 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44.0,\n            'f': \"44.0\",\n        },\n\"Niet invasieve bloeddruk diastolisch\",\n\"mmHg\",\n{\n            'v': 64559220000,\n            'f': \"64559220000\",\n        },\n{\n            'v': 64560054506,\n            'f': \"64560054506\",\n        },\n\"<NA>\"],\n [{\n            'v': 2387512,\n            'f': \"2387512\",\n        },\n{\n            'v': 99,\n            'f': \"99\",\n        },\n{\n            'v': 16.1,\n            'f': \"16.1\",\n        },\n\"Anion-Gap (bloed)\",\n\"mmol/l\",\n{\n            'v': 64559220000,\n            'f': \"64559220000\",\n        },\n{\n            'v': 64560126490,\n            'f': \"64560126490\",\n        },\n\"<NA>\"],\n [{\n            'v': 12282570,\n            'f': \"12282570\",\n        },\n{\n            'v': 99,\n            'f': \"99\",\n        },\n{\n            'v': 46.0,\n            'f': \"46.0\",\n        },\n\"pCO2 (bloed)\",\n\"mmHg\",\n{\n            'v': 64559220000,\n            'f': \"64559220000\",\n    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<tbody>\n","    <tr>\n","      <th>491254</th>\n","      <td>99</td>\n","      <td>3.06</td>\n","      <td>Prothrombinetijd  (bloed)</td>\n","      <td>INR</td>\n","      <td>63537960000</td>\n","      <td>64553427023</td>\n","      <td>&lt;NA&gt;</td>\n","    </tr>\n","    <tr>\n","      <th>12921069</th>\n","      <td>99</td>\n","      <td>2.23</td>\n","      <td>Prothrombinetijd  (bloed)</td>\n","      <td>INR</td>\n","      <td>64137240000</td>\n","      <td>64553435943</td>\n","      <td>&lt;NA&gt;</td>\n","    </tr>\n","    <tr>\n","      <th>18242771</th>\n","      <td>99</td>\n","      <td>5.70</td>\n","      <td>Kalium (bloed)</td>\n","      <td>mmol/l</td>\n","      <td>64137540000</td>\n","      <td>64553442396</td>\n","      <td>&lt;NA&gt;</td>\n","    </tr>\n","    <tr>\n","      <th>2443985</th>\n","      <td>99</td>\n","      <td>623.00</td>\n","      <td>Kreatinine (bloed)</td>\n","      <td>µmol/l</td>\n","      <td>64519920000</td>\n","      <td>64553621723</td>\n","      <td>&lt;NA&gt;</td>\n","    </tr>\n","    <tr>\n","      <th>18214944</th>\n","      <td>99</td>\n","      <td>336.00</td>\n","      <td>Thrombo's (bloed)</td>\n","      <td>10^9/l</td>\n","      <td>64519920000</td>\n","      <td>64553620710</td>\n","      <td>&lt;NA&gt;</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>18799206</th>\n","      <td>99</td>\n","      <td>45.00</td>\n","      <td>Niet invasieve bloeddruk diastolisch</td>\n","      <td>mmHg</td>\n","      <td>64563720000</td>\n","      <td>64565760000</td>\n","      <td>&lt;NA&gt;</td>\n","    </tr>\n","    <tr>\n","      <th>5134665</th>\n","      <td>99</td>\n","      <td>89.00</td>\n","      <td>Hartfrequentie</td>\n","      <td>/min</td>\n","      <td>64566420000</td>\n","      <td>64567320000</td>\n","      <td>&lt;NA&gt;</td>\n","    </tr>\n","    <tr>\n","      <th>8572211</th>\n","      <td>99</td>\n","      <td>25.00</td>\n","      <td>Niet invasieve bloeddruk diastolisch</td>\n","      <td>mmHg</td>\n","      <td>64567260000</td>\n","      <td>64567320000</td>\n","      <td>&lt;NA&gt;</td>\n","    </tr>\n","    <tr>\n","      <th>11271379</th>\n","      <td>99</td>\n","      <td>62.00</td>\n","      <td>Niet invasieve bloeddruk gemiddeld</td>\n","      <td>mmHg</td>\n","      <td>64567260000</td>\n","      <td>64567320000</td>\n","      <td>&lt;NA&gt;</td>\n","    </tr>\n","    <tr>\n","      <th>8596423</th>\n","      <td>99</td>\n","      <td>95.00</td>\n","      <td>Niet invasieve bloeddruk systolisch</td>\n","      <td>mmHg</td>\n","      <td>64567260000</td>\n","      <td>64567320000</td>\n","      <td>&lt;NA&gt;</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>88 rows × 7 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div 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Kreatinine (bloed)           µmol/l   \n","18214944                     Thrombo's (bloed)           10^9/l   \n","...                                        ...              ...   \n","18799206  Niet invasieve bloeddruk diastolisch             mmHg   \n","5134665                         Hartfrequentie             /min   \n","8572211   Niet invasieve bloeddruk diastolisch             mmHg   \n","11271379    Niet invasieve bloeddruk gemiddeld             mmHg   \n","8596423    Niet invasieve bloeddruk systolisch             mmHg   \n","\n","          measuredat_numericitem  updatedat_numericitem  fluidout_numericitem  \n","491254               63537960000            64553427023                  <NA>  \n","12921069             64137240000            64553435943                  <NA>  \n","18242771             64137540000            64553442396                  <NA>  \n","2443985              64519920000            64553621723                  <NA>  \n","18214944             64519920000            64553620710                  <NA>  \n","...                          ...                    ...                   ...  \n","18799206             64563720000            64565760000                  <NA>  \n","5134665              64566420000            64567320000                  <NA>  \n","8572211              64567260000            64567320000                  <NA>  \n","11271379             64567260000            64567320000                  <NA>  \n","8596423              64567260000            64567320000                  <NA>  \n","\n","[88 rows x 7 columns]"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["numericitems_table[numericitems_table['admissionid']==99].sort_values(\"measuredat_numericitem\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"o0SPIAFJkZF9"},"outputs":[],"source":["listitem_table = pd.read_gbq(\n","    '''\n","    SELECT admissionid, value AS value_listitem, item AS item_listitem, measuredat AS measuredat_listitem, updatedat AS updatedat_listitem\n","    FROM listitems\n","    WHERE CAST(admissionid AS STRING) IN UNNEST(@list_admission_ids)\n","    '''\n","    , configuration=query_config, use_bqstorage_api=True).sort_values(\"admissionid\")\n"]},{"cell_type":"markdown","metadata":{"id":"wBlfxuvY1Ow6"},"source":["# Final table join"]},{"cell_type":"markdown","metadata":{"id":"75zCDgKzqU8o"},"source":["## Table for each patient"]},{"cell_type":"code","execution_count":40,"metadata":{"executionInfo":{"elapsed":43,"status":"ok","timestamp":1701955702526,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"4qqDP5eCQsYy"},"outputs":[],"source":["list_of_items = [\n"," 'Ht (bloed)',\n"," 'Ht(v.Bgs) (bloed)',\n"," 'Hemoglobine',\n"," 'Hb (bloed)',\n"," 'Leucocyten',\n"," \"Leuco's (bloed)\",\n"," 'Thrombocyten',\n"," \"Thrombo's (bloed)\",\n"," 'Bili Totaal',\n"," 'Bilirubine (bloed)',\n"," 'Kreatinine',\n"," 'Kreatinine (bloed)',\n"," 'Laktaat',\n"," 'Lactaat (bloed)',\n"," 'Ademfrequentie Spontaan',\n"," 'Natrium (bloed)',\n"," 'Kalium (bloed)']"]},{"cell_type":"code","execution_count":59,"metadata":{"executionInfo":{"elapsed":347,"status":"ok","timestamp":1701958515590,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"SIU-HtaogJbt"},"outputs":[],"source":["supplement_items = ['Hartfrequentie', 'Saturatie (Monitor)']"]},{"cell_type":"code","execution_count":48,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":32568,"status":"ok","timestamp":1701956214931,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"zneWGRTfMi2_","outputId":"fded4a2e-10f3-4415-9617-b27dde654190"},"outputs":[{"name":"stdout","output_type":"stream","text":["{'Ht (bloed)': 34068, 'Ht(v.Bgs) (bloed)': 84166, 'Hemoglobine': 1329, 'Hb (bloed)': 36950, 'Leucocyten': 1227, \"Leuco's (bloed)\": 34217, 'Thrombocyten': 1265, \"Thrombo's (bloed)\": 35225, 'Bili Totaal': 427, 'Bilirubine (bloed)': 12808, 'Kreatinine': 1217, 'Kreatinine (bloed)': 35336, 'Laktaat': 391, 'Lactaat (bloed)': 28948, 'Ademfrequentie Spontaan': 6283, 'Natrium (bloed)': 40373, 'Kalium (bloed)': 39710}\n"]}],"source":["dic = {}\n","for item in list_of_items:\n","  num = numericitems_table[(numericitems_table['admissionid'].isin(int_admission_ids)) & (numericitems_table['item_numericitem'] == item)].shape[0]\n","  dic[item] = num\n","print(dic)"]},{"cell_type":"code","execution_count":49,"metadata":{"executionInfo":{"elapsed":1574,"status":"ok","timestamp":1701956224360,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"u6BoCoXWYae-"},"outputs":[],"source":["df_in_list = numericitems_table[(numericitems_table['admissionid'].isin(int_admission_ids)) & (numericitems_table['item_numericitem'].isin(list_of_items))]"]},{"cell_type":"code","execution_count":52,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":669},"executionInfo":{"elapsed":235,"status":"ok","timestamp":1701956255308,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"AjhPCizpYxuL","outputId":"16e7509d-1af8-4ccf-95db-7223c497a73b"},"outputs":[{"data":{"application/vnd.google.colaboratory.module+javascript":"\n      import \"https://ssl.gstatic.com/colaboratory/data_table/881c4a0d49046431/data_table.js\";\n\n      const table = window.createDataTable({\n        data: [[{\n            'v': 16918862,\n            'f': \"16918862\",\n        },\n{\n            'v': 11,\n            'f': \"11\",\n        },\n{\n            'v': 337.0,\n            'f': \"337.0\",\n        },\n\"Kreatinine (bloed)\",\n\"\\u00b5mol/l\",\n{\n            'v': 29400000,\n            'f': \"29400000\",\n        },\n{\n            'v': 34173580,\n            'f': \"34173580\",\n        },\n\"<NA>\"],\n [{\n            'v': 5876356,\n            'f': \"5876356\",\n        },\n{\n            'v': 11,\n            'f': \"11\",\n        },\n{\n            'v': 4.6999998,\n            'f': \"4.6999998\",\n        },\n\"Lactaat (bloed)\",\n\"mmol/l\",\n{\n            'v': 72240000,\n            'f': \"72240000\",\n        },\n{\n            'v': 75096150,\n            'f': \"75096150\",\n        },\n\"<NA>\"],\n [{\n            'v': 12682852,\n            'f': \"12682852\",\n        },\n{\n            'v': 11,\n            'f': \"11\",\n        },\n{\n            'v': 144.0,\n            'f': \"144.0\",\n        },\n\"Natrium (bloed)\",\n\"mmol/l\",\n{\n            'v': 116520000,\n            'f': \"116520000\",\n        },\n{\n            'v': 121498543,\n            'f': \"121498543\",\n        },\n\"<NA>\"],\n [{\n            'v': 16263416,\n            'f': \"16263416\",\n        },\n{\n            'v': 11,\n            'f': \"11\",\n        },\n{\n            'v': 0.34,\n            'f': \"0.34\",\n        },\n\"Ht(v.Bgs) (bloed)\",\n\"Geen\",\n{\n            'v': 86640000,\n            'f': \"86640000\",\n        },\n{\n            'v': 85602900,\n            'f': \"85602900\",\n        },\n\"<NA>\"],\n [{\n            'v': 10920053,\n            'f': \"10920053\",\n        },\n{\n            'v': 11,\n            'f': \"11\",\n        },\n{\n            'v': 0.27000001,\n            'f': \"0.27000001\",\n        },\n\"Ht(v.Bgs) (bloed)\",\n\"Geen\",\n{\n            'v': 5040000,\n            'f': \"5040000\",\n        },\n{\n            'v': 6226040,\n            'f': \"6226040\",\n        },\n\"<NA>\"],\n [{\n            'v': 10800973,\n            'f': \"10800973\",\n        },\n{\n            'v': 11,\n            'f': \"11\",\n        },\n{\n            'v': 10.4,\n            'f': \"10.4\",\n        },\n\"Leuco's (bloed)\",\n\"10^9/l\",\n{\n            'v': 116520000,\n            'f': \"116520000\",\n        },\n{\n            'v': 117320026,\n            'f': \"117320026\",\n        },\n\"<NA>\"],\n [{\n            'v': 5718199,\n            'f': \"5718199\",\n        },\n{\n            'v': 11,\n            'f': \"11\",\n        },\n{\n            'v': 127.0,\n            'f': \"127.0\",\n        },\n\"Thrombo's (bloed)\",\n\"10^9/l\",\n{\n            'v': 4020000,\n            'f': \"4020000\",\n        },\n{\n            'v': 5148620,\n            'f': \"5148620\",\n        },\n\"<NA>\"],\n [{\n            'v': 16633942,\n            'f': \"16633942\",\n        },\n{\n            'v': 11,\n            'f': \"11\",\n        },\n{\n            'v': 0.27000001,\n            'f': \"0.27000001\",\n        },\n\"Ht(v.Bgs) (bloed)\",\n\"Geen\",\n{\n            'v': -3360000,\n            'f': \"-3360000\",\n        },\n{\n            'v': 1437026,\n            'f': \"1437026\",\n        },\n\"<NA>\"],\n [{\n            'v': 6832100,\n            'f': \"6832100\",\n     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discard date<0\n","    table = table[table['measuredat_numericitem'] >= 0]\n","\n","    timestamps = np.sort(table[(table['admissionid']==id) & (table['item_numericitem'].isin(features))]['measuredat_numericitem'].unique()).tolist()\n","    admittedat = septic_patients.loc[septic_patients['admissionid']==id].iloc[-1]['admittedat']\n","    timestamp_list = [t for t in timestamps if t >= admittedat]\n","    timestamp_list.insert(0, admittedat)\n","    merged_df = pd.DataFrame({'timestamp': timestamp_list})\n","\n","    for item in features:\n","      measure_item_id = table[(table['admissionid']==id) & (table['item_numericitem']==item)].loc[:, ['measuredat_numericitem','value_numericitem']].sort_values(by='measuredat_numericitem')\n","      measure_item_id.rename(columns={'value_numericitem': item, 'measuredat_numericitem': 'timestamp'}, inplace=True)\n","      merged_df = pd.merge(merged_df, measure_item_id, on='timestamp', how='left') # 2 cols: timestamp + value (for one admission one item)\n","\n","    merged_df.insert(0, 'admissionid', id) # timestamp + one column per feature, adding admissionid\n","    merged_df['timestamp'] = pd.to_datetime(merged_df['timestamp'], unit='ms') # convert timestamp to datetime format\n","\n","    # df_numeric = pd.concat([df_numeric, merged_df]) # take it back without saving\n","    count += 1\n","\n","    df_numeric = pd.concat([df_numeric, merged_df]) # delete without saving\n","    # change this line if want 3D data\n","  # df_numeric = df_numeric.set_index('timestamp').groupby('admissionid').resample(time_interval).last().fillna(method='ffill') # .max(), .mean(), .mode()\n","\n","\n","  return df_numeric\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2075103,"status":"ok","timestamp":1701967952014,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"c3wjTrkBiqz9","outputId":"ba2c4d6e-0a28-4dcf-8249-4831fc3ee49f"},"outputs":[],"source":["df_numeric = get_numeric_dataframe(df_in_list, TIME_INTERVAL, list_of_items, septic_patients)"]},{"cell_type":"code","execution_count":110,"metadata":{"executionInfo":{"elapsed":3635,"status":"ok","timestamp":1701968079244,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"10CFGfPGoozF"},"outputs":[],"source":["df_supplement = numericitems_table[(numericitems_table['admissionid'].isin(int_admission_ids)) & (numericitems_table['item_numericitem'].isin(supplement_items))]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":758591,"status":"ok","timestamp":1701968841268,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"-lJR9wPUopiK","outputId":"6bd3a1f4-54c1-4915-ea75-e646de0e21db"},"outputs":[],"source":["df_numeric = get_numeric_dataframe(df_supplement, TIME_INTERVAL, supplement_items, septic_patients)"]},{"cell_type":"code","execution_count":113,"metadata":{"executionInfo":{"elapsed":2,"status":"ok","timestamp":1701969092399,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"LbM1rt8rfxEa"},"outputs":[],"source":["IMPUTE_COLUMNS = {'Ht(v.Bgs) (bloed)':'Ht (bloed)',\n","                  'Hb (bloed)':'Hemoglobine',\n","                  'Leuco\\'s (bloed)':'Leucocyten',\n","                  'Thrombo\\'s (bloed)':'Thrombocyten',\n","                  'Bilirubine (bloed)':'Bili Totaal',\n","                  'Kreatinine (bloed)':'Kreatinine',\n","                  'Lactaat (bloed)':'Laktaat'\n","          }"]},{"cell_type":"code","execution_count":114,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":359,"status":"ok","timestamp":1701969093977,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"OB5OYvHGfqy4","outputId":"4c10ab11-5c6c-4cc0-c5ba-50d419583062"},"outputs":[{"name":"stdout","output_type":"stream","text":["43769\n","20119\n","95323\n","94106\n","97288\n","96169\n","95798\n","94646\n","113329\n","112937\n","96726\n","95616\n","97753\n","97397\n","Warning: total number of rows (123470) exceeds max_rows (30000). Falling back to pandas display.\n"]},{"data":{"text/html":["\n","  <div id=\"df-eb33fe97-4b2f-4b36-a884-8b33f4809696\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>admissionid</th>\n","      <th>timestamp</th>\n","      <th>Ht(v.Bgs) (bloed)</th>\n","      <th>Hb (bloed)</th>\n","      <th>Leuco's (bloed)</th>\n","      <th>Thrombo's (bloed)</th>\n","      <th>Bilirubine (bloed)</th>\n","      <th>Kreatinine (bloed)</th>\n","      <th>Lactaat (bloed)</th>\n","      <th>Ademfrequentie Spontaan</th>\n","      <th>Natrium (bloed)</th>\n","      <th>Kalium (bloed)</th>\n","    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<td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>151.0</td>\n","      <td>4.6</td>\n","    </tr>\n","    <tr>\n","      <th>123468</th>\n","      <td>23535</td>\n","      <td>1970-01-08 03:33:00</td>\n","      <td>0.29</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>123469</th>\n","      <td>23535</td>\n","      <td>1970-01-08 04:58:00</td>\n","      <td>0.29</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>123470 rows × 12 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div 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1970-01-08 04:58:00               0.29         NaN   \n","\n","        Leuco's (bloed)  Thrombo's (bloed)  Bilirubine (bloed)  \\\n","0                   NaN                NaN                 NaN   \n","1                   8.9              127.0                17.0   \n","2                   NaN                NaN                 NaN   \n","3                   NaN                NaN                 NaN   \n","4                   NaN                NaN                 NaN   \n","...                 ...                ...                 ...   \n","123465              NaN                NaN                 NaN   \n","123466              5.4              261.0                 NaN   \n","123467              NaN                NaN                 NaN   \n","123468              NaN                NaN                 NaN   \n","123469              NaN                NaN                 NaN   \n","\n","        Kreatinine (bloed)  Lactaat (bloed)  Ademfrequentie Spontaan  \\\n","0                      NaN              NaN                      NaN   \n","1                    350.0              5.9                      NaN   \n","2                      NaN              NaN                      NaN   \n","3                      NaN              NaN                      NaN   \n","4                      NaN              NaN                      NaN   \n","...                    ...              ...                      ...   \n","123465                 NaN              NaN                      NaN   \n","123466               130.0              NaN                      NaN   \n","123467                 NaN              NaN                      NaN   \n","123468                 NaN              NaN                      NaN   \n","123469                 NaN              NaN                      NaN   \n","\n","        Natrium (bloed)  Kalium (bloed)  \n","0                   NaN             NaN  \n","1                 136.0             3.9  \n","2                   NaN             NaN  \n","3                   NaN             NaN  \n","4                   NaN             NaN  \n","...                 ...             ...  \n","123465            149.0             5.0  \n","123466            149.0             4.6  \n","123467            151.0             4.6  \n","123468              NaN             NaN  \n","123469              NaN             NaN  \n","\n","[123470 rows x 12 columns]"]},"execution_count":114,"metadata":{},"output_type":"execute_result"}],"source":["for col1, col2 in IMPUTE_COLUMNS.items():\n","  print(df_numeric[col1].isna().sum())\n","  df_numeric[col1] = df_numeric[col1].fillna(df_numeric[col2])\n","  df_numeric = df_numeric.drop([col2], axis=1)\n","  print(df_numeric[col1].isna().sum())\n","df_numeric"]},{"cell_type":"code","execution_count":115,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1512},"executionInfo":{"elapsed":236,"status":"ok","timestamp":1701969101143,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"kdqB03fo_Wak","outputId":"cd6f7328-c4f9-4bec-f0c7-75381fd04ec7"},"outputs":[{"data":{"application/vnd.google.colaboratory.module+javascript":"\n      import \"https://ssl.gstatic.com/colaboratory/data_table/881c4a0d49046431/data_table.js\";\n\n      const table = window.createDataTable({\n        data: [[{\n            'v': 110,\n            'f': \"110\",\n        },\n{\n            'v': 47,\n            'f': \"47\",\n        },\n\"1970-01-02 22:08:00\",\n{\n            'v': NaN,\n            'f': \"NaN\",\n        },\n{\n            'v': NaN,\n            'f': \"NaN\",\n        },\n{\n            'v': NaN,\n            'f': \"NaN\",\n        },\n{\n            'v': NaN,\n            'f': \"NaN\",\n        },\n{\n            'v': NaN,\n            'f': \"NaN\",\n        },\n{\n            'v': NaN,\n            'f': \"NaN\",\n        },\n{\n            'v': NaN,\n            'f': \"NaN\",\n        },\n{\n            'v': NaN,\n 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       NaN   \n","121           47  1970-01-03 21:27:00               0.27         NaN   \n","122           47  1970-01-03 22:44:00               0.25         NaN   \n","123           47  1970-01-03 23:49:00               0.25         NaN   \n","124           47  1970-01-04 02:48:00               0.25         NaN   \n","125           47  1970-01-04 05:59:00               0.25         NaN   \n","126           47  1970-01-04 06:05:00               0.24         4.9   \n","127           47  1970-01-04 12:04:00               0.27         NaN   \n","128           47  1970-01-04 15:04:00               0.27         NaN   \n","129           47  1970-01-04 17:27:00               0.27         NaN   \n","130           47  1970-01-04 21:20:00               0.27         NaN   \n","131           47  1970-01-05 00:41:00               0.27         NaN   \n","132           47  1970-01-05 06:05:00                NaN         5.8   \n","133           47  1970-01-05 06:12:00               0.28         NaN   \n","134           47  1970-01-05 09:36:00               0.27         NaN   \n","135           47  1970-01-05 11:47:00               0.28         NaN   \n","136           47  1970-01-05 14:08:00               0.27         NaN   \n","137           47  1970-01-05 17:35:00               0.28         NaN   \n","138           47  1970-01-05 23:41:00               0.28         NaN   \n","139           47  1970-01-06 02:24:00               0.28         NaN   \n","140           47  1970-01-06 06:05:00               0.28         5.8   \n","141           47  1970-01-06 09:18:00               0.27         NaN   \n","142           47  1970-01-06 13:03:00               0.27         NaN   \n","143           47  1970-01-06 22:10:00               0.25         NaN   \n","144           47  1970-01-06 23:40:00               0.25         NaN   \n","145           47  1970-01-07 03:05:00               0.25         NaN   \n","146           47  1970-01-07 06:05:00                NaN         5.1   \n","147           47  1970-01-07 06:16:00               0.26         NaN   \n","148           47  1970-01-07 09:57:00               0.26         NaN   \n","\n","     Leuco's (bloed)  Thrombo's (bloed)  Bilirubine (bloed)  \\\n","110              NaN                NaN                 NaN   \n","111              NaN                NaN                 NaN   \n","112              NaN              120.0                 NaN   \n","113              NaN                NaN                 NaN   \n","114             15.3              208.0                14.0   \n","115              NaN                NaN                 NaN   \n","116              NaN                NaN                 NaN   \n","117             14.5              254.0                 NaN   \n","118              NaN                NaN                 NaN   \n","119              NaN                NaN                 NaN   \n","120              NaN                NaN                 NaN   \n","121              NaN                NaN                 NaN   \n","122              NaN                NaN                 NaN   \n","123              NaN                NaN                 NaN   \n","124              NaN                NaN                 NaN   \n","125              NaN                NaN                 NaN   \n","126              9.2              128.0                 7.0   \n","127              NaN                NaN                 NaN   \n","128              NaN                NaN                 NaN   \n","129              NaN                NaN                 NaN   \n","130              NaN                NaN                 NaN   \n","131              NaN                NaN                 NaN   \n","132              9.8              115.0                 8.0   \n","133              NaN                NaN                 NaN   \n","134              NaN                NaN                 NaN   \n","135              NaN                NaN                 NaN   \n","136              NaN                NaN                 NaN   \n","137              NaN                NaN                 NaN   \n","138              NaN                NaN                 NaN   \n","139              NaN                NaN                 NaN   \n","140              6.5               93.0                 9.0   \n","141              NaN                NaN                 NaN   \n","142              NaN                NaN                 NaN   \n","143              NaN                NaN                 NaN   \n","144              NaN                NaN                 NaN   \n","145              NaN                NaN                 NaN   \n","146              5.5               98.0                 5.0   \n","147              NaN                NaN                 NaN   \n","148              NaN                NaN                 NaN   \n","\n","     Kreatinine (bloed)  Lactaat (bloed)  Ademfrequentie Spontaan  \\\n","110                 NaN              NaN                      NaN   \n","111                 NaN              2.6                      NaN   \n","112                 NaN              NaN                      NaN   \n","113                 NaN              2.8                      NaN   \n","114                80.0              NaN                      NaN   \n","115                 NaN              3.6                      NaN   \n","116                 NaN              4.4                      NaN   \n","117               106.0              NaN                      NaN   \n","118                 NaN              4.2                      NaN   \n","119                 NaN              3.2                      NaN   \n","120                 NaN              3.3                      NaN   \n","121                 NaN              3.8                      NaN   \n","122                 NaN              3.3                      NaN   \n","123                 NaN              3.1                      NaN   \n","124                 NaN              2.4                      NaN   \n","125                 NaN              1.8                      NaN   \n","126                76.0              NaN                      NaN   \n","127                 NaN              1.7                      NaN   \n","128                 NaN              1.4                      NaN   \n","129                 NaN              1.6                      NaN   \n","130                 NaN              1.5                      NaN   \n","131                 NaN              1.4                      NaN   \n","132                51.0              NaN                      NaN   \n","133                 NaN              1.1                      NaN   \n","134                 NaN              1.3                      NaN   \n","135                 NaN              1.3                      NaN   \n","136                 NaN              1.1                      NaN   \n","137                 NaN              1.0                      NaN   \n","138                 NaN              0.8                      NaN   \n","139                 NaN              1.0                      NaN   \n","140                64.0              0.9                      NaN   \n","141                 NaN              1.0                      NaN   \n","142                 NaN              0.9                      NaN   \n","143                 NaN              1.3                      NaN   \n","144                 NaN              1.4                      NaN   \n","145                 NaN              1.3                      NaN   \n","146                63.0              NaN                      NaN   \n","147                 NaN              1.4                      NaN   \n","148                 NaN              1.5                      NaN   \n","\n","     Natrium (bloed)  Kalium (bloed)  \n","110              NaN             NaN  \n","111              NaN             NaN  \n","112              NaN             NaN  \n","113              NaN             NaN  \n","114              NaN             NaN  \n","115              NaN             NaN  \n","116              NaN             NaN  \n","117            138.0             4.8  \n","118              NaN             NaN  \n","119              NaN             NaN  \n","120              NaN             NaN  \n","121              NaN             NaN  \n","122              NaN             NaN  \n","123              NaN             NaN  \n","124              NaN             NaN  \n","125              NaN             NaN  \n","126              NaN             NaN  \n","127              NaN             NaN  \n","128              NaN             NaN  \n","129              NaN             NaN  \n","130              NaN             NaN  \n","131              NaN             NaN  \n","132              NaN             NaN  \n","133              NaN             NaN  \n","134              NaN             NaN  \n","135              NaN             NaN  \n","136              NaN             NaN  \n","137              NaN             NaN  \n","138              NaN             NaN  \n","139              NaN             NaN  \n","140              NaN             NaN  \n","141              NaN             NaN  \n","142              NaN             NaN  \n","143              NaN             NaN  \n","144              NaN             NaN  \n","145              NaN             NaN  \n","146              NaN             NaN  \n","147              NaN             NaN  \n","148              NaN             NaN  "]},"execution_count":115,"metadata":{},"output_type":"execute_result"}],"source":["df_numeric[df_numeric['admissionid']==47]"]},{"cell_type":"code","execution_count":116,"metadata":{"executionInfo":{"elapsed":690,"status":"ok","timestamp":1701969107184,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"wDAVQqm0iQNN"},"outputs":[],"source":["df_numeric = pd.merge_ordered(df_numeric, df_supplement, on=['admissionid','timestamp'], fill_method='ffill', how='left')"]},{"cell_type":"code","execution_count":117,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":826},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1701969108064,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"xF1jq3TQZITA","outputId":"dfc1190a-1baf-46c1-935b-4bd5e5a0171e"},"outputs":[{"name":"stdout","output_type":"stream","text":["Warning: total number of rows (123558) exceeds max_rows (30000). Falling back to pandas display.\n"]},{"data":{"text/html":["\n","  <div id=\"df-27c7dc7d-e3c8-467a-b6ad-aa6f52766585\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>admissionid</th>\n","      <th>timestamp</th>\n","      <th>Ht(v.Bgs) (bloed)</th>\n","      <th>Hb (bloed)</th>\n","      <th>Leuco's (bloed)</th>\n","      <th>Thrombo's (bloed)</th>\n","      <th>Bilirubine (bloed)</th>\n","      <th>Kreatinine (bloed)</th>\n","      <th>Lactaat (bloed)</th>\n","      <th>Ademfrequentie Spontaan</th>\n","      <th>Natrium (bloed)</th>\n","      <th>Kalium (bloed)</th>\n","      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<td>0.27</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>143.0</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>11</td>\n","      <td>1970-01-01 03:04:00</td>\n","      <td>0.28</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>145.0</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>11</td>\n","      <td>1970-01-01 04:04:00</td>\n","      <td>0.28</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>141.0</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>123553</th>\n","      <td>23535</td>\n","      <td>1970-01-07 00:51:00</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>149.0</td>\n","      <td>5.0</td>\n","      <td>109.0</td>\n","      <td>96.0</td>\n","    </tr>\n","    <tr>\n","      <th>123554</th>\n","      <td>23535</td>\n","      <td>1970-01-07 17:43:00</td>\n","      <td>0.32</td>\n","      <td>6.4</td>\n","      <td>5.4</td>\n","      <td>261.0</td>\n","      <td>NaN</td>\n","      <td>130.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>149.0</td>\n","      <td>4.6</td>\n","      <td>121.0</td>\n","      <td>97.0</td>\n","    </tr>\n","    <tr>\n","      <th>123555</th>\n","      <td>23535</td>\n","      <td>1970-01-07 23:20:00</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>151.0</td>\n","      <td>4.6</td>\n","      <td>121.0</td>\n","      <td>97.0</td>\n","    </tr>\n","    <tr>\n","      <th>123556</th>\n","      <td>23535</td>\n","      <td>1970-01-08 03:33:00</td>\n","      <td>0.29</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>121.0</td>\n","      <td>97.0</td>\n","    </tr>\n","    <tr>\n","      <th>123557</th>\n","      <td>23535</td>\n","      <td>1970-01-08 04:58:00</td>\n","      <td>0.29</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>121.0</td>\n","      <td>97.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>123558 rows × 14 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-27c7dc7d-e3c8-467a-b6ad-aa6f52766585')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path 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1970-01-08 04:58:00               0.29         NaN   \n","\n","        Leuco's (bloed)  Thrombo's (bloed)  Bilirubine (bloed)  \\\n","0                   NaN                NaN                 NaN   \n","1                   8.9              127.0                17.0   \n","2                   NaN                NaN                 NaN   \n","3                   NaN                NaN                 NaN   \n","4                   NaN                NaN                 NaN   \n","...                 ...                ...                 ...   \n","123553              NaN                NaN                 NaN   \n","123554              5.4              261.0                 NaN   \n","123555              NaN                NaN                 NaN   \n","123556              NaN                NaN                 NaN   \n","123557              NaN                NaN                 NaN   \n","\n","        Kreatinine (bloed)  Lactaat (bloed)  Ademfrequentie Spontaan  \\\n","0                      NaN              NaN                      NaN   \n","1                    350.0              5.9                      NaN   \n","2                      NaN              NaN                      NaN   \n","3                      NaN              NaN                      NaN   \n","4                      NaN              NaN                      NaN   \n","...                    ...              ...                      ...   \n","123553                 NaN              NaN                      NaN   \n","123554               130.0              NaN                      NaN   \n","123555                 NaN              NaN                      NaN   \n","123556                 NaN              NaN                      NaN   \n","123557                 NaN              NaN                      NaN   \n","\n","        Natrium (bloed)  Kalium (bloed)  Hartfrequentie  Saturatie (Monitor)  \n","0                   NaN             NaN             NaN                  NaN  \n","1                 136.0             3.9             NaN                  NaN  \n","2                   NaN             NaN           143.0                  NaN  \n","3                   NaN             NaN           145.0                  NaN  \n","4                   NaN             NaN           141.0                  NaN  \n","...                 ...             ...             ...                  ...  \n","123553            149.0             5.0           109.0                 96.0  \n","123554            149.0             4.6           121.0                 97.0  \n","123555            151.0             4.6           121.0                 97.0  \n","123556              NaN             NaN           121.0                 97.0  \n","123557              NaN             NaN           121.0                 97.0  \n","\n","[123558 rows x 14 columns]"]},"execution_count":117,"metadata":{},"output_type":"execute_result"}],"source":["df_numeric"]},{"cell_type":"code","execution_count":126,"metadata":{"executionInfo":{"elapsed":5387,"status":"ok","timestamp":1701876374882,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"zJHyhFjYLqSD"},"outputs":[],"source":["df_numeric['timestamp'] = pd.to_datetime(df_numeric['timestamp'])\n","df_numeric = df_numeric.set_index('timestamp').groupby('admissionid').resample('1H').last().fillna(method='ffill').drop(columns=['admissionid']).reset_index()\n","df_numeric = df_numeric.set_index('timestamp').groupby('admissionid').resample('8H').last().fillna(method='ffill').fillna(method='bfill').drop(columns=['admissionid']).reset_index()\n","first_admission = df_numeric.groupby('admissionid')['timestamp'].min().reset_index()\n","first_admission.columns = ['admissionid','first_admission']\n","df = pd.merge(df_numeric, first_admission, on='admissionid', how='left')\n","df['time'] = df['timestamp']-df['first_admission']\n","df_numeric['timestamp'] = df['time'].dt.total_seconds()//3600"]},{"cell_type":"code","execution_count":81,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1512},"executionInfo":{"elapsed":448,"status":"ok","timestamp":1701964120414,"user":{"displayName":"X Yan","userId":"15158429978909086245"},"user_tz":-60},"id":"W3uFuKrd901Z","outputId":"48283fef-e461-4485-d157-341ebd7da452"},"outputs":[{"data":{"application/vnd.google.colaboratory.module+javascript":"\n      import \"https://ssl.gstatic.com/colaboratory/data_table/881c4a0d49046431/data_table.js\";\n\n      const table = window.createDataTable({\n        data: [[{\n            'v': 106,\n            'f': \"106\",\n        },\n{\n            'v': 47,\n            'f': \"47\",\n        },\n\"1970-01-01 06:06:00\",\n{\n            'v': 0.31,\n            'f': \"0.31\",\n        },\n{\n            'v': 6.3000002,\n            'f': \"6.3000002\",\n    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<td>47</td>\n","      <td>1970-01-07 06:16:00</td>\n","      <td>0.26</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>1.4</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>72.0</td>\n","      <td>91.0</td>\n","    </tr>\n","    <tr>\n","      <th>147</th>\n","      <td>47</td>\n","      <td>1970-01-07 09:57:00</td>\n","      <td>0.26</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>1.5</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>72.0</td>\n","      <td>91.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-442dcbdc-b6ba-4392-a91d-8da0b3101d4b')\"\n","            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       6.8   \n","117           47  1970-01-03 09:32:00               0.30         NaN   \n","118           47  1970-01-03 15:10:00               0.28         NaN   \n","119           47  1970-01-03 17:24:00               0.29         NaN   \n","120           47  1970-01-03 21:27:00               0.27         NaN   \n","121           47  1970-01-03 22:44:00               0.25         NaN   \n","122           47  1970-01-03 23:49:00               0.25         NaN   \n","123           47  1970-01-04 02:48:00               0.25         NaN   \n","124           47  1970-01-04 05:59:00               0.25         NaN   \n","125           47  1970-01-04 06:05:00               0.24         4.9   \n","126           47  1970-01-04 12:04:00               0.27         NaN   \n","127           47  1970-01-04 15:04:00               0.27         NaN   \n","128           47  1970-01-04 17:27:00               0.27         NaN   \n","129           47  1970-01-04 21:20:00               0.27         NaN   \n","130           47  1970-01-05 00:41:00               0.27         NaN   \n","131           47  1970-01-05 06:05:00                NaN         5.8   \n","132           47  1970-01-05 06:12:00               0.28         NaN   \n","133           47  1970-01-05 09:36:00               0.27         NaN   \n","134           47  1970-01-05 11:47:00               0.28         NaN   \n","135           47  1970-01-05 14:08:00               0.27         NaN   \n","136           47  1970-01-05 17:35:00               0.28         NaN   \n","137           47  1970-01-05 23:41:00               0.28         NaN   \n","138           47  1970-01-06 02:24:00               0.28         NaN   \n","139           47  1970-01-06 06:05:00               0.28         5.8   \n","140           47  1970-01-06 09:18:00               0.27         NaN   \n","141           47  1970-01-06 13:03:00               0.27         NaN   \n","142           47  1970-01-06 22:10:00               0.25         NaN   \n","143           47  1970-01-06 23:40:00               0.25         NaN   \n","144           47  1970-01-07 03:05:00               0.25         NaN   \n","145           47  1970-01-07 06:05:00                NaN         5.1   \n","146           47  1970-01-07 06:16:00               0.26         NaN   \n","147           47  1970-01-07 09:57:00               0.26         NaN   \n","\n","     Leuco's (bloed)  Thrombo's (bloed)  Bilirubine (bloed)  \\\n","106             12.9              119.0                 NaN   \n","107              NaN                NaN                 NaN   \n","108              8.5               83.0                 8.0   \n","109              NaN                NaN                 NaN   \n","110              NaN                NaN                 NaN   \n","111              NaN              120.0                 NaN   \n","112              NaN                NaN                 NaN   \n","113             15.3              208.0                14.0   \n","114              NaN                NaN                 NaN   \n","115              NaN                NaN                 NaN   \n","116             14.5              254.0                 NaN   \n","117              NaN                NaN                 NaN   \n","118              NaN                NaN                 NaN   \n","119              NaN                NaN                 NaN   \n","120              NaN                NaN                 NaN   \n","121              NaN                NaN                 NaN   \n","122              NaN                NaN                 NaN   \n","123              NaN                NaN                 NaN   \n","124              NaN                NaN                 NaN   \n","125              9.2              128.0                 7.0   \n","126              NaN                NaN                 NaN   \n","127              NaN                NaN                 NaN   \n","128              NaN                NaN                 NaN   \n","129              NaN                NaN                 NaN   \n","130              NaN                NaN                 NaN   \n","131              9.8              115.0                 8.0   \n","132              NaN                NaN                 NaN   \n","133              NaN                NaN                 NaN   \n","134              NaN                NaN                 NaN   \n","135              NaN                NaN                 NaN   \n","136              NaN                NaN                 NaN   \n","137              NaN                NaN                 NaN   \n","138              NaN                NaN                 NaN   \n","139              6.5               93.0                 9.0   \n","140              NaN                NaN                 NaN   \n","141              NaN                NaN                 NaN   \n","142              NaN                NaN                 NaN   \n","143              NaN                NaN                 NaN   \n","144              NaN                NaN                 NaN   \n","145              5.5               98.0                 5.0   \n","146              NaN                NaN                 NaN   \n","147              NaN                NaN                 NaN   \n","\n","     Kreatinine (bloed)  Lactaat (bloed)  Ademfrequentie Spontaan  \\\n","106                73.0              NaN                      NaN   \n","107                 NaN              2.8                      NaN   \n","108                64.0              NaN                      NaN   \n","109                 NaN              NaN                      NaN   \n","110                 NaN              2.6                      NaN   \n","111                 NaN              NaN                      NaN   \n","112                 NaN              2.8                      NaN   \n","113                80.0              NaN                      NaN   \n","114                 NaN              3.6                      NaN   \n","115                 NaN              4.4                      NaN   \n","116               106.0              NaN                      NaN   \n","117                 NaN              4.2                      NaN   \n","118                 NaN              3.2                      NaN   \n","119                 NaN              3.3                      NaN   \n","120                 NaN              3.8                      NaN   \n","121                 NaN              3.3                      NaN   \n","122                 NaN              3.1                      NaN   \n","123                 NaN              2.4                      NaN   \n","124                 NaN              1.8                      NaN   \n","125                76.0              NaN                      NaN   \n","126                 NaN              1.7                      NaN   \n","127                 NaN              1.4                      NaN   \n","128                 NaN              1.6                      NaN   \n","129                 NaN              1.5                      NaN   \n","130                 NaN              1.4                      NaN   \n","131                51.0              NaN                      NaN   \n","132                 NaN              1.1                      NaN   \n","133                 NaN              1.3                      NaN   \n","134                 NaN              1.3                      NaN   \n","135                 NaN              1.1                      NaN   \n","136                 NaN              1.0                      NaN   \n","137                 NaN              0.8                      NaN   \n","138                 NaN              1.0                      NaN   \n","139                64.0              0.9                      NaN   \n","140                 NaN              1.0                      NaN   \n","141                 NaN              0.9                      NaN   \n","142                 NaN              1.3                      NaN   \n","143                 NaN              1.4                      NaN   \n","144                 NaN              1.3                      NaN   \n","145                63.0              NaN                      NaN   \n","146                 NaN              1.4                      NaN   \n","147                 NaN              1.5                      NaN   \n","\n","     Natrium (bloed)  Kalium (bloed)  Hartfrequentie  Saturatie (Monitor)  \n","106            141.0             4.3            94.0                  NaN  \n","107              NaN             NaN            94.0                  NaN  \n","108            139.0             4.0            94.0                  NaN  \n","109              NaN             NaN             NaN                  NaN  \n","110              NaN             NaN             NaN                  NaN  \n","111              NaN             NaN             NaN                  NaN  \n","112              NaN             NaN             NaN                  NaN  \n","113              NaN             NaN             NaN                  NaN  \n","114              NaN             NaN             NaN                  NaN  \n","115              NaN             NaN             NaN                  NaN  \n","116            138.0             4.8             NaN                  NaN  \n","117              NaN             NaN             NaN                  NaN  \n","118              NaN             NaN             NaN                  NaN  \n","119              NaN             NaN             NaN                  NaN  \n","120              NaN             NaN             NaN                  NaN  \n","121              NaN             NaN             NaN                  NaN  \n","122              NaN             NaN             NaN                  NaN  \n","123              NaN             NaN             NaN                  NaN  \n","124              NaN             NaN             NaN                  NaN  \n","125              NaN             NaN            89.0                 94.0  \n","126              NaN             NaN            89.0                 94.0  \n","127              NaN             NaN            89.0                 94.0  \n","128              NaN             NaN            89.0                 94.0  \n","129              NaN             NaN            89.0                 94.0  \n","130              NaN             NaN            89.0                 94.0  \n","131              NaN             NaN            70.0                 97.0  \n","132              NaN             NaN            70.0                 97.0  \n","133              NaN             NaN            70.0                 97.0  \n","134              NaN             NaN            70.0                 97.0  \n","135              NaN             NaN            70.0                 97.0  \n","136              NaN             NaN            70.0                 97.0  \n","137              NaN             NaN            70.0                 97.0  \n","138              NaN             NaN            70.0                 97.0  \n","139              NaN             NaN            86.0                 93.0  \n","140              NaN             NaN            86.0                 93.0  \n","141              NaN             NaN            86.0                 93.0  \n","142              NaN             NaN            86.0                 93.0  \n","143              NaN             NaN            86.0                 93.0  \n","144              NaN             NaN            76.0                 96.0  \n","145              NaN             NaN            72.0                 91.0  \n","146              NaN             NaN            72.0                 91.0  \n","147              NaN             NaN            72.0                 91.0  "]},"execution_count":81,"metadata":{},"output_type":"execute_result"}],"source":["df_numeric[df_numeric['admissionid']==47]"]},{"cell_type":"markdown","metadata":{"id":"LS9ip9yi7RSZ"},"source":["# Labels (mortality)\n","after complete dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"c5W86Wf27bpL"},"outputs":[],"source":["# labels for training and testing\n","labels_list = []\n","for id in np.sort(complete_data['admissionid'].unique()).tolist():\n","  if septic_patients[septic_patients['admissionid'] == id]['destination']=='Overleden':\n","    labels_list.append(1)\n","  else:\n","    labels_list.append(0)\n","labels = pd.DataFrame({'admissionid':np.sort(complete_data['admissionid'].unique()), 'label':labels_list})"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"xs0BBi_9FhmN"},"outputs":[],"source":["# labels' timestamps for LSTM SHAP\n","# use the last timestamp as the time of death\n","label_time_list = []\n","for id in np.sort(complete_data['admissionid'].unique()).tolist():\n","  label_time_list.append(complete_data[complete_data['admissionid']==id]['time'][-1])"]},{"cell_type":"markdown","metadata":{"id":"KECZk6TfBBHn"},"source":["## Capping, Normalization"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"bsFlr5D7D5vf"},"outputs":[],"source":["# Impute possible remaining missing values\n","\n","column_name = list(X.columns)[1:]\n","for feature in column_name:\n","  # X.loc[X[feature].isnull(), feature] = X.icustay_id.map(fast_mode(X, ['icustay_id'], feature).set_index('icustay_id')[feature])\n","  X.loc[:,feature] = X.loc[:,feature].fillna(method='ffill')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6g8C05MvBEzR"},"outputs":[],"source":["# Capping outliers\n","\n","cap_mask = X.columns.difference(['admissionid', 'timestamp', 'dose'])\n","cap_mask = NUMERIC_FEATURES\n","X[cap_mask] = X[cap_mask].clip(X[cap_mask].quantile(0.01), X[cap_mask].quantile(0.99), axis=1)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"AbC70Dr6FYg1"},"outputs":[],"source":["# Normalize into [0,1]\n","\n","norm_mask = NUMERIC_FEATURES\n","X[norm_mask] = (X[norm_mask] - X[norm_mask].min()) / (X[norm_mask].max() - X[norm_mask].min())"]},{"cell_type":"markdown","metadata":{"id":"Y0vyEJxKGBBL"},"source":["## Train-test split"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"wqw87Hm7GdGY"},"outputs":[],"source":["from sklearn.model_selection import train_test_split\n","\n","id_list = list(X['admissionid'].unique())\n","SPLIT_SIZE = 0.2"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gj5TDS0kGAa0"},"outputs":[],"source":["id_train, id_test_val = train_test_split(id_list, test_size = SPLIT_SIZE, random_state = 42) # train set is 80%)\n","print(\"train is %d\" % len(id_train))\n","# remaining 20% split in halves as test and validation 10% and 10%\n","id_valid, id_test = train_test_split(id_test_val, test_size = 0.5, random_state = 42) # test 10% valid 10%\n","print(\"val and test are %d\" %len(id_test))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CRlG4sNfNBHR"},"outputs":[],"source":["train = X[X.admissionid.isin(id_train)].sort_values(by=['admissionid'])\n","test = X[X.admissionid.isin(id_test)].sort_values(by=['admissionid'], ignore_index = True)\n","validation = X[X.admissionid.isin(id_valid)].sort_values(by=['admissionid'])\n","\n","test = test.sort_values(by=['admissionid', 'timestamp'], ignore_index = True)\n","train = train.sort_values(by=['admissionid', 'timestamp'], ignore_index = True)\n","validation = validation.sort_values(by=['admissionid', 'timestamp'], ignore_index = True)"]}],"metadata":{"colab":{"provenance":[{"file_id":"1GhfwUnuJLJs4iOrVtggykE6hurJhQKlk","timestamp":1701076530846},{"file_id":"1DIvw-qrx2nu3_C_0T96Oz-ka5Or8NQxL","timestamp":1700648774580}]},"kernelspec":{"display_name":"Python 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