A Simple and Interpretable Predictive Model for Healthcare

Subhadip Maji, Raghav Bali, Sree Harsha Ankem, Kishore V. Ayyadevara

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 & deploy and adversely impact real world usage. We address these challenges by developing a simpler yet interpretable tree based model. We model and showcase 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. Our work shows an improvement over deep learning based solutions all the while maintaining interpretability.