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

arXiv: https://arxiv.org/abs/2309.06584 · HTML: https://mlsanity.com/paper/2309.06584/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 42 references that are in the map.
- First posted on arXiv 12 Sep 2023.

## Builds on (most cited first, 10 of 10)

- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [GNNExplainer: Generating Explanations for Graph Neural Networks](https://mlsanity.com/paper/1903.03894/) (2019)
- [A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability](https://mlsanity.com/paper/2204.08570/) (2022)
- [FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation](https://mlsanity.com/paper/2102.04925/) (2021)
- [Graph Representation Learning in Biomedicine](https://mlsanity.com/paper/2104.04883/) (2021)
- [Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations](https://mlsanity.com/paper/1906.05017/) (2019)
- [DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations](https://mlsanity.com/paper/2107.02467/) (2021)
- [Mining On Alzheimer's Diseases Related Knowledge Graph to Identity Potential AD-related Semantic Triples for Drug Repurposing](https://mlsanity.com/paper/2202.08712/) (2022)
- [TMHOI: Translational Model for Human-Object Interaction Detection](https://mlsanity.com/paper/2303.04253/) (2023)
- [Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2306.10456/) (2023)

## Built on by (most cited first, 4 of 4)

- [GNN Explanations that do not Explain and How to find Them](https://mlsanity.com/paper/2601.20815/) (2026)
- [CA-BERT: Leveraging Context Awareness for Enhanced Multi-Turn Chat Interaction](https://mlsanity.com/paper/2409.13701/) (2024)
- [AD-CDO: A Lightweight Ontology for Representing Eligibility Criteria in Alzheimer's Disease Clinical Trials](https://mlsanity.com/paper/2511.21724/) (2025)
- [Early Prediction of Alzheimer's and Related Dementias: A Machine Learning Approach Utilizing Social Determinants of Health Data](https://mlsanity.com/paper/2503.16560/) (2025)

## Abstract (arXiv)

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.