Interpretable Predictive Models in the Healthcare Domain

Presented by Sridharan Kamalakannan, Head of Data Science at Humana

Predictive models are often used to identify individuals that will likely have escalating health severity in the future and accordingly deliver appropriate interventions. However, for the clinicians and care managers, these predictive models often act as a black-box at an individual level. The reason for this being, typically predictive models use combinations of complicated algorithms that makes it hard to explain the reason behind a predictive model score at an individual level. This talk will focus on model and feature agnostic methodologies and techniques that help uncover the drivers behind a prediction at a personal level in a healthcare setting.