Abstract:Activated PI3K8 Syndrome (APDS) is a rare genetic immune disorder caused by variants in PIK3CD or PIK3R1, with highly heterogeneous symptoms that often delay diagnosis. Early recognition is hampered by overlapping clinical presentations and limited clinician awareness, motivating systematic, data-driven approaches to detect APDS-associated phenotypic patterns in routine electronic health records. Traditional linear scoring systems cannot capture complex symptom interactions, while deep learning models, though expressive, often lack interpretability. To bridge this gap, we propose Shapley regression, a novel game-theoretic model replacing the linear predictor with a k-additive cooperative game, explicitly modeling co-occurrence of symptoms while maintaining the transparency and convexity of logistic regression. We carry out an empirical study of our lightweight method on eight public biomedical datasets, showing that a 2-additive model with $l_{2}$ regularization achieves an optimal trade-off between predictive power and noise robustness. We also apply it to a real-world cohort of 222 patients, on which Shapley regression accurately distinguished APDS cases from matched controls, confirming and validating phenotypes known to be associated with APDS, and facilitating the exploration of pairwise interactions between symptoms, validated by clinical experts.
| Comments: | 21 pages, 4 figures. Accepted to the AI and Health special track at IJCAI 2026; the first two named authors had equal contribution |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| ACM classes: | I.2.6; J.3 |
| Cite as: | arXiv:2605.08897 [cs.LG] |
| (or arXiv:2605.08897v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.08897 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Tomás Brogueira [view email]
[v1]
Sat, 9 May 2026 11:30:54 UTC (413 KB)