基于入院数据预测老年人住院时间过长，提升医院管理与干预效率。
Explainable Admission-Level Predictive Modeling for Prolonged Hospital Stay in Elderly Populations: Challenges in Low- and Middle-Income Countries
- 用信息量和图论选9个可解释特征，提升模型透明度。
- 验证集上准确率76%、敏感性64%、AUC达0.82，表现稳健。
- 适合资源有限地区医院用于优化床位安排与干预设计。
住院时间过长（pLoS）是院内不良事件的重要风险因素。本文利用入院时患者及医院行政数据，构建并解释了预测pLoS的模型。方法采用特征选择技术，筛选非相关且信息量最高的特征，结合图论中团内权重证据法选取代表性变量。研究分析了2017年1月至2022年3月间来自Antioquia大学医院的120,354条住院记录，经清洗后划分为训练（67%）、测试（22%）和验证（11%）三组。使用逻辑回归模型预测住院天数是否超过7天。评估指标包括准确率、精确率、敏感性、特异性及AUC-ROC。特征选择最终获得9个可解释变量，显著增强模型透明性。在验证集上，模型特异性为0.83（95% CI: 0.82–0.84），敏感性为0.64（95% CI: 0.62–0.65），准确率为0.76（95% CI: 0.76–0.77），精确率为0.67（95% CI: 0.66–0.69），AUC-ROC为0.82（95% CI: 0.81–0.83）。模型具有强预测能力，并揭示影响长期住院的关键因素，可为医院管理及未来干预研究提供有力工具。
原文摘要 · Abstract (English)
Prolonged length of stay (pLoS) is a significant factor associated with the risk of adverse in-hospital events. We develop and explain a predictive model for pLos using admission-level patient and hospital administrative data. The approach includes a feature selection method by selecting non-correlated features with the highest information value. The method uses features weights of evidence to select a representative within cliques from graph theory. The prognosis study analyzed the records from 120,354 hospital admissions at the Hospital Alma Mater de Antioquia between January 2017 and March 2022. After a cleaning process the dataset was split into training (67%), test (22%), and validation (11%) cohorts. A logistic regression model was trained to predict the pLoS in two classes: less than or greater than 7 days. The performance of the model was evaluated using accuracy, precision, sensitivity, specificity, and AUC-ROC metrics. The feature selection method returns nine interpretable variables, enhancing the models' transparency. In the validation cohort, the pLoS model achieved a specificity of 0.83 (95% CI, 0.82-0.84), sensitivity of 0.64 (95% CI, 0.62-0.65), accuracy of 0.76 (95% CI, 0.76-0.77), precision of 0.67 (95% CI, 0.66-0.69), and AUC-ROC of 0.82 (95% CI, 0.81-0.83). The model exhibits strong predictive performance and offers insights into the factors that influence prolonged hospital stays. This makes it a valuable tool for hospital management and for developing future intervention studies aimed at reducing pLoS.
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