References

References for Practitioners

Here are references for clinicians, analysts, and developers outlining some of our approaches and guidelines in developing and evaluating various machine-learning models.

Evaluation of machine-learning models

Introduction to evaluation of ML models

I. Accuracy Metrics

II. Assessment of Calibration

III. Interpretability

IV. Uncertainty

Reinforcement learning

Reinforcement learning for clinical decisions

Iterative Dataset Creation for Decision Analysis

Iterative creation of clinical cohort trajectories