A Simple and Interpretable Predictive Model for Healthcare

TL;DR

This paper introduces a simple, interpretable, non-deep learning model for healthcare prediction tasks, specifically predicting the first occurrence of a diagnosis, outperforming complex deep learning models in accuracy while maintaining interpretability.

Contribution

The paper develops a tree-based, interpretable model for disease prediction that requires less data and compute, serving as a strong baseline for future research.

Findings

Outperforms deep learning models in accuracy for diagnosis prediction

Maintains interpretability while improving performance

Reduces computational and data requirements

Abstract

Deep Learning based models are currently dominating most state-of-the-art solutions for disease prediction. Existing works employ RNNs along with multiple levels of attention mechanisms to provide interpretability. These deep learning models, with trainable parameters running into millions, require huge amounts of compute and data to train and deploy. These requirements are sometimes so huge that they render usage of such models as unfeasible. We address these challenges by developing a simpler yet interpretable non-deep learning based model for application to EHR data. We model and showcase our work's results on the task of predicting first occurrence of a diagnosis, often overlooked in existing works. We push the capabilities of a tree based model and come up with a strong baseline for more sophisticated models. Its performance shows an improvement over deep learning based solutions (both, with and without the first-occurrence constraint) all the while maintaining interpretability.