Pharma KOL Identification for NeuroShield Launch

Developed an advanced scoring and network model using Bayesian analysis to identify Key Opinion Leaders (KOLs) for a neurology product launch, utilizing clinical trials data, research publications, leadership positions, and open payment data. Secured a top-three position among 42 teams.

Table of Contents

• Project Overview
• Project Highlights
  • Data Collection
  • Data Preprocessing
  • Exploratory Data Analysis (EDA)
  • Model Building
  • Recommendations
• Outcome

Project Overview

In a case competition hosted by ProcDNA, I led a team to develop an advanced scoring and network model using Bayesian analysis to identify influential Key Opinion Leaders (KOLs) for Bright Life’s NeuroShield launch. NeuroShield is a wearable, non-invasive device designed for early Parkinson’s intervention therapy.

Project Highlights

Data Collection

• Extracted data from clinical trials, research publications, leadership positions, and open payment records using Python libraries and APIs.

Data Preprocessing

• Cleansed and structured the data for enhanced reliability, performed feature engineering, and conducted outlier detection.

Exploratory Data Analysis (EDA)

• Identified trends and correlations to inform feature selection and optimization.

Model Building

• Created a heuristic scoring model and network model to rank KOLs based on their influence using Bayesian analysis.

Recommendations

• Developed a strategic plan for engaging top KOLs to support the product launch, ensuring ongoing accuracy and adaptability through a dashboard.

Outcome

• Secured a top-three position among 42 teams in the competition.
• Provided a comprehensive analysis methodology and strategic insights for the successful launch and adoption of NeuroShield.