Self-explainable Graph Neural Network for Alzheimer's Disease And Related Dementias Risk Prediction

TL;DR

This paper introduces a self-explainable graph neural network model that improves Alzheimer's disease risk prediction using claims data and provides interpretable insights into the key medical relationships influencing the risk.

Contribution

It presents a novel VGNN model with relation importance evaluation, enhancing interpretability and prediction accuracy in ADRD risk assessment from claims data.

Findings

VGNN outperformed baselines by 10% in ROC AUC.

The model offers interpretable insights into medical code relationships.

The approach has potential applications beyond ADRD risk prediction.

Abstract

Background: Alzheimer's disease and related dementias (ADRD) ranks as the sixth leading cause of death in the US, underlining the importance of accurate ADRD risk prediction. While recent advancement in ADRD risk prediction have primarily relied on imaging analysis, yet not all patients undergo medical imaging before an ADRD diagnosis. Merging machine learning with claims data can reveal additional risk factors and uncover interconnections among diverse medical codes. Objective: Our goal is to utilize Graph Neural Networks (GNNs) with claims data for ADRD risk prediction. Addressing the lack of human-interpretable reasons behind these predictions, we introduce an innovative method to evaluate relationship importance and its influence on ADRD risk prediction, ensuring comprehensive interpretation. Methods: We employed Variationally Regularized Encoder-decoder Graph Neural Network (VGNN) for estimating ADRD likelihood. We created three scenarios to assess the model's efficiency, using Random Forest and Light Gradient Boost Machine as baselines. We further used our relation importance method to clarify the key relationships for ADRD risk prediction. Results: VGNN surpassed other baseline models by 10% in the area under the receiver operating characteristic. The integration of the GNN model and relation importance interpretation could potentially play an essential role in providing valuable insight into factors that may contribute to or delay ADRD progression. Conclusions: Employing a GNN approach with claims data enhances ADRD risk prediction and provides insights into the impact of interconnected medical code relationships. This methodology not only enables ADRD risk modeling but also shows potential for other image analysis predictions using claims data.