The availability and use of disease, real world, and population-level datasets, are essential for AI in research and healthcare. Researchers can train AI models to derive useful insights, enhance clinical decision-making, and improve patient outcomes.
In this whitepaper, we examine the approaches and best practices for using disease, real world, and population-level data to train AI models.
Key white paper highlights include:
- The importance of data preprocessing, standardization and feature engineering,
- Aspects of AI model training and validation, and
- Real world applications and case studies from health research and drug discovery fields.
Download the full white paper now and discover the future of training AI models using disease, real world and population level data with Lifebit.