RiskPath: Explainable deep learning for multistep biomedical prediction in longitudinal data

Many diseases are the end outcomes of multifactorial risks that interact and increment over months or years. Time-series AI methods have attracted increasing interest given their ability to operate on native time-series data to predict disease outcomes. Instantiating such models in risk stratification tools has proceeded more slowly, in part limited by factors such as structural complexity, model size, and explainability. Here, we present RiskPath, an explainable AI toolbox that offers advanced time-series methods and additional functionality relevant to risk stratification use cases in classic and emerging longitudinal cohorts. Theoretically informed optimization is integrated in prediction to specify optimal model topology or explore performance-complexity trade-offs. Accompanying modules allow the user to map the changing importance of predictors over the disease course, visualize the most important antecedent time epochs contributing to disease risk, or remove predictors to construct compact models for clinical applications with minimal performance impact.