Explainable Graph Neural Networks in Intensive Care Unit Mortality Prediction: Edge-Level and Motif-Level Analysis

Öz

Accurate forecasting of ICU patient outcomes is essential for clinical decision support. However, most high-performing machine learning models function as black boxes, limiting their interpretability and clinical adoption. This study introduces a graph-based explainable risk-prediction framework, in which patient–patient relations are modeled through a diagnosis-based similarity network. An undirected graph was derived from the eICU-CRD demo subset (PhysioNet v2.0) by linking individuals sharing three-digit ICD-9 categories, and a GCN was trained for in-hospital mortality prediction. Despite the dataset’s modest size and imbalance, meaningful discrimination was achieved (AUROC = 0.708; AUPRC = 0.308). A two-layer explainability analysis was applied to clarify the model’s decision process. Each prediction was driven by a combination of patient-specific clinical attributes and signals from a small number of influential neighbors, according to GNNExplainer. SubgraphX, a Shapley-value-based motif discovery method, identified compact and clinically coherent subgraphs with strong causal influence on the prediction. Consistency between edge- and motif-level explanations indicated that the model relies on stable relational patterns with clinical relevance. These findings suggest that integrating GNNs with structured explainability methods can transform a single risk score into a transparent, data-driven decision-support mechanism that provides interpretable and hypothesis-generating insights to clinicians.

Anahtar Kelimeler

• Explainable Graph Neural Networks
• GNNExplainer
• SubgraphX

Kaynakça

1. Adadi, A. and Berrada, M. (2018). Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI). IEEE Access, 6, 52138–52160. https://doi.org/10.1109/ACCESS.2018.2870052 google scholar
2. Breiman, L. (2001). Statistical modeling: The two cultures. Statistical Science, 16(3), 199–215. doi: 10.1214/SS/1009213726 google scholar
3. Buchanan, B. G., & Shortliffe, E. H. (Eds.). (1984). Rule-based expert systems: The MYCIN experiments of the Stanford heuristic programming project (Addison Wesley, Rea…). Retrieved from https://www.shortliffe.net/Buchanan-Shortliffe-1984/MYCIN%20Book.htm google scholar
4. Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., and Elhadad, N. (2015). Intelligible models for healthcare: Predicting the risk of pneumonia and 30-day hospital readmission !emph[Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining];, 2015-August, 1721–1730. https://doi.org/10.1145/2783258.2788613/SUPPL_FILE/P1721.MP4 google scholar
5. Goldberger, A. L., Amaral, L. A., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., … Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 101(23). https://doi.org/10.1161/01.CIR.101.23.E215 google scholar
6. Lin, K.-W., Kuo, Y.-C., Wang, H.-Y., & Tseng, Y.-J. (2025). KAT-GNN: A Knowledge-Augmented Temporal Graph Neural Network for Risk Prediction in Electronic Health Records Retrieved from https://arxiv.org/pdf/2511.01249 google scholar
7. Lu, H., & Uddin, S. (2021). A weighted patient network-based framework for predicting chronic diseases using GNs Scientific Reports, 11(1), 22607. DOI: 10.1038/S41598-021-01964-2 google scholar
8. Lundberg, S. M., Erion, G., Chen, H., Degrave, A., Prutkin, J. M., Nair, B., … Lee, S.-I. (n.d.). From Local Explanations to Global Understanding with Explainable Tree AI https://github.com/suinleelab/treeexplainer-study google scholar

9. Mao, C., Yao, L., & Luo, Y. (2022). MedGCN: Medication recommendation and lab test imputation via graph convolutional networks Journal of Biomedical Informatics, 127, 104000. DOI: 10.1016/J.JBI.2022.104000 google scholar
10. Pollard, T. J., Johnson, A. E. W., Raffa, J. D., Celi, L. A., Mark, R. G., & Badawi, O. (2018). The eICU collaborative research database is a freely available multi-center database for critical care research. Scientific Data, 5, https://doi.org/10.1038/SDATA.2018.178, google scholar
11. Sağıroğlu, Ş., & Demirezen, M. U. (2022). Yapay Zekâ ve Büyük Veri Kitap Serisi 4: Yorumlanabilir ve Açıklanabilir Yapay Zekâ ve Güncel Konular. google scholar
12. Sahu, M. K., & Roy, P. (2025). Similarity-Based Self-Construct Graph Model for Predicting Patient Criticalness Using Graph Neural Networks and Electronic Health Record Data Retrieved from https://arxiv.org/pdf/2508.00615 google scholar
13. Shapley, L. S. (1952). Value for n-person games The Shapley Value, (28), 307–317. https://doi.org/10.1017/CBO9780511528446.003 google scholar
14. Tjoa, E., & Guan, C. (2021). A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI. IEEE Transactions on Neural Networks and Learning Systems, 32(11), 4793–4813. https://doi.org/10.1109/TNNLS.2020.3027314 google scholar
15. Tong, C., Rocheteau, E., Veličković, P., Lane, N., & Liò, P., 2021. Predicting Patient Outcomes Using Graph Representation Learning Studies in Computational Intelligence, 1013, 281–293. DOI: 10.1007/978-3-030-93080-6_20 google scholar
16. Van Noorden, R., & Perkel, J. M. (2023). AI and science: What 1,600 researchers think. Nature, 621(7980), 672–675. https://doi.org/10.1038/D41586-023-02980-0https://doi.org/10.1038/D41586-023-02980-0 google scholar
17. Watson, D. S. (2022). Statistics of Interpretable Machine Learning Digital Ethics Lab Yearbook, 133–155. doi: 10.1007/978-3-031-09846-8_10 google scholar
18. Ying R, Bourgeois D, You J, Zitnik, M., & Leskovec, J. (2019). GNNExplainer: Generating Explanations for Graph Neural Networks. Advances in Neural Information Processing Systems, 32. Retrieved from https://arxiv.org/pdf/1903.03894 google scholar
19. The yuan H, Yu H, Wang J, Li, K., & Ji, S. (2021). Explainability of Graph Neural Networks via Subgraph Exploration The yuan H, Yu H, Wang J, Li, K., & Ji, S. (2021). Explainability of Graph Neural Networks via Subgraph Exploration google scholar