构建可解释、公平且可观测的住院再入院预测框架，提升临床可用性。
An Integrated Framework for Explainable, Fair, and Observable Hospital Readmission Prediction: Development and Validation on MIMIC-IV
- 融合逻辑回归与梯度提升树，用SHAP提供个体化解释。
- 模型AUC达0.696，各人群公平性差异小于阈值。
- 适合关注医疗公平性与模型透明度的研究者使用。
目的：提出并回顾性验证一个集成框架，解决再入院预测临床转化中的三大障碍：缺乏可解释性、部署可靠性基础设施缺失、以及人口学公平性评估不足。方法：基于MIMIC-IV数据库构建415,231例成人住院队列（30天再入院率18.0%），按70/15/15划分数据集。在26个特征上训练逻辑回归、XGBoost与LightGBM模型，采用SHAP生成个体患者解释。通过AUC-ROC、假阴性率（FNR）与阳性预测值（PPV）在16个亚组中评估公平性；使用布里尔分数与校准曲线评估校准性能。结果：XGBoost取得AUC-ROC 0.696（95% CI 0.691–0.701），优于或匹配LACE基线（AUC 0.60–0.68）。LightGBM表现最佳校准性（布里尔分数0.146）。既往住院史为最重要预测因子。所有亚组均满足公平性标准（ΔAUC ≤ 0.05，ΔFNR ≤ 0.10）。结论：该框架具备竞争性性能、临床可操作的解释能力及强人口学公平性。代码已公开于https://github.com/Tomisin92/readmission-prediction。
原文摘要 · Abstract (English)
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 https://github.com/Tomisin92/readmission-prediction.
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