White Papers
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.
Empty feature stores shouldn’t be the obstacle that keeps HCOs from using ML to proactively address preventable, negative health outcomes and reduce costs. Purchasing or building a feature store is essential to scale and utilize ML implementations for predictive modeling. Read the white paper to learn how a healthcare feature store can accelerate time-to-value.
Following large tech companies, healthcare organizations are beginning to leverage machine learning (ML) systems to proactively improve health outcomes and reduce costs by enabling predictive insights from diverse data sets. To this end, purchasing or building a feature store is essential to scale and utilize ML implementations for predictive modeling.
Download the white paper to learn how:
- Healthcare feature stores can provide explainability and transparency for clinical experts in a low-code environment
- Solutions built for healthcare with populated feature stores accelerate time-to-value
- Feature stores solve key data management and version control challenges by ensuring that features are defined consistently
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Related Resources
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.
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.
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.
