Interpretable Survival Analysis for Heart Failure Risk Prediction
Mike Van Ness Tomas Bosschieter Natasha Din Andrew Ambrosy Alexander Sandhu Madeleine Udell
Survival analysis, or time-to-event analysis, is an important and widespread
problem in healthcare research. Medical research has traditionally relied on
Cox models for survival analysis, due to their simplicity and interpretability.
Cox models assume a log-linear hazard function as well as proportional hazards
over time, and can perform poorly when these assumptions fail. Newer survival
models based on machine learning avoid these assumptions and offer improved
accuracy, yet sometimes at the expense of model interpretability, which is
vital for clinical use. We propose a novel survival analysis pipeline that is
both interpretable and competitive with state-of-the-art survival models.
Specifically, we use an improved version of survival stacking to transform a
survival analysis problem to a classification problem, ControlBurn to perform
feature selection, and Explainable Boosting Machines to generate interpretable
predictions. To evaluate our pipeline, we predict risk of heart failure using a
large-scale EHR database. Our pipeline achieves state-of-the-art performance
and provides interesting and novel insights about risk factors for heart
failure.