Making machine learning matter to clinicians: An actionable model for medical decision-making
We propose a measure that measures the ability of a model to increase the probability of medical decision making by reducing uncertainty in specific clinical scenarios. In practice, we envision this metric being used during the early stages of model development (i.e., before net benefit is calculated) for multilayered models in dynamic care settings such … Read more