Pharma KOL Identification
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.
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.