Interpretable Hierarchical Attention Network for Medical Condition Identification

TL;DR

This paper introduces an Interpretable Hierarchical Attention Network (IHAN) that improves prediction accuracy and interpretability of medical conditions by aligning with medical data structure and providing clear insights for healthcare professionals.

Contribution

The paper presents a novel hierarchical attention deep learning model tailored for medical data, enhancing both prediction performance and interpretability over existing methods.

Findings

Effective prediction of stage 3 CKD using medical history data.

Hierarchical attention structure aligns with medical data sequence.

Model provides interpretable contribution scores for medical events.

Abstract

Accurate prediction of medical conditions with straight past clinical evidence is a long-sought topic in the medical management and health insurance field. Although great progress has been made with machine learning algorithms, the medical community is still skeptical about the model accuracy and interpretability. This paper presents an innovative hierarchical attention deep learning model to achieve better prediction and clear interpretability that can be easily understood by medical professionals. This paper developed an Interpretable Hierarchical Attention Network (IHAN). IHAN uses a hierarchical attention structure that matches naturally with the medical history data structure and reflects patients encounter (date of service) sequence. The model attention structure consists of 3 levels: (1) attention on the medical code types (diagnosis codes, procedure codes, lab test results, and prescription drugs), (2) attention on the sequential medical encounters within a type, (3) attention on the individual medical codes within an encounter and type. This model is applied to predict the occurrence of stage 3 chronic kidney disease (CKD), using three years medical history of Medicare Advantage (MA) members from an American nationwide health insurance company. The model takes members medical events, both claims and Electronic Medical Records (EMR) data, as input, makes a prediction of stage 3 CKD and calculates contribution from individual events to the predicted outcome.