Life Science — Clinical Data ML Model Training Pipeline

Overview

The purpose of this DAG is to train machine learning models using clinical data to predict patient outcomes, thereby facilitating improved customer personalization in the life sciences sector. The data sources include electronic health records, clinical trial data, and patient demographic information. The ingestion pipeline begins with data extraction from these sources, followed by data cleansing and transformation to ensure high-quality inputs for model training. Processing steps include cross

The purpose of this DAG is to train machine learning models using clinical data to predict patient outcomes, thereby facilitating improved customer personalization in the life sciences sector. The data sources include electronic health records, clinical trial data, and patient demographic information. The ingestion pipeline begins with data extraction from these sources, followed by data cleansing and transformation to ensure high-quality inputs for model training. Processing steps include cross-validation for model evaluation, hyperparameter tuning for optimal performance, and model selection based on predefined criteria. Quality controls are implemented throughout the pipeline to monitor data integrity and model performance. The outputs of this DAG are the trained machine learning models, which are stored in a feature store for future use, along with performance metrics such as model accuracy and training duration. Monitoring KPIs include model precision, recall, and the time taken for each training cycle. The business value lies in enabling healthcare organizations to make data-driven decisions, ultimately improving patient care and enhancing the personalization of services offered to clients.

Part of the Pricing Optimization solution for the Life Science industry.

Use cases

• Improves patient outcomes through predictive insights
• Enhances customer engagement with personalized services
• Reduces time to market for new therapies
• Facilitates compliance with regulatory standards
• Drives innovation in clinical research and development