White Papers
Six Mistakes You Can Avoid in Healthcare Data Science
Download the white paper for detailed insights into some of the most common errors healthcare data scientists make, why they make them, and the ways to avoid them.
Healthcare data scientists must confront a host of challenges that do not exist in other industries. The fact that many data scientists come to healthcare from non-healthcare backgrounds means they will not be familiar with the subtle-yet-vital details waiting for them.
Learn the six most common mistakes made in healthcare data science. Download the white paper for detailed insights into some of the most common errors healthcare data scientists make, why they make them, and the ways to avoid them including:
- Not predicting impactable risk
- Not anticipating deployment
- Data leakage
- Inadvertently introducing bias and more
Read the white paper
Related Resources
Where Most Healthcare AI/ML Deployments Go Wrong
Read the white paper to explore three of the most common ways healthcare AI/ML models go wrong, and how you can ensure they go well.
Machine Learning in Healthcare: Will Traditional Feature Stores Work?
Read the white paper to learn how a healthcare feature store can accelerate time-to-value.
Why Most Fairness Metrics Don’t Work in Healthcare AI/ML
Selecting an appropriate definition of fairness is difficult for healthcare algorithms, as they are applied to myriad diverse problems. Read the paper to learn why we need different definitions of fairness and to understand the most ideal fairness metric for population health AI/ML.
