Abstract:Objective: To propose and retrospectively validate an integrated framework addressing three barriers to clinical translation of readmission prediction: lack of explainability, absence of deployment reliability infrastructure, and inadequate demographic fairness evaluation.
Materials and Methods: We constructed a cohort of 415231 adult admissions from the MIMIC-IV database (30-day readmission prevalence 18.0%), split 70/15/15. Logistic regression, XGBoost, and LightGBM models were trained on 26 features. SHAP provided per-patient explanations. Fairness was evaluated across 16 subgroups using AUC-ROC, false negative rate (FNR), and positive predictive value (PPV). Calibration was assessed using Brier scores and calibration curves.
Results: XGBoost achieved AUC-ROC 0.696 (95% CI 0.691-0.701), outperforming or matching the LACE baseline (AUC 0.60-0.68). LightGBM achieved best calibration (Brier 0.146). Prior admissions were the dominant predictor. All subgroups met equity thresholds (delta AUC <= 0.05, delta FNR <= 0.10).
Conclusion: This framework delivers competitive performance, clinically actionable explanations, and strong demographic equity. Code is publicly available at this https URL.
| Comments: | 22 pages, 8 figures. Submitted to the Journal of the American Medical Informatics Association (JAMIA), currently under review |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2604.22535 [cs.LG] |
| (or arXiv:2604.22535v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2604.22535 arXiv-issued DOI via DataCite (pending registration) |
From: Isaac Adisa [view email]
[v1]
Fri, 24 Apr 2026 13:21:44 UTC (526 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。