可解释的入院层面预测建模用于老年人群延长住院时间：低收入和中等收入国家的挑战
Explainable Admission-Level Predictive Modeling for Prolonged Hospital Stay in Elderly Populations: Challenges in Low- and Middle-Income Countries
AI总结：
本文提出了一种可解释的预测模型，用于预测老年人群延长住院时间，通过特征选择方法提升模型透明性，验证集结果显示模型具有良好的预测性能。
摘要：
延长住院时间（pLoS）是住院期间不良事件风险的重要因素。我们开发并解释了一个使用入院层面的患者和医院行政数据来预测pLoS的预测模型。该方法包括通过选择与目标变量不相关的、信息价值最高的特征来进行特征选择。该方法使用证据权重来选择图论中的代表性特征。 prognostic研究分析了2017年1月到2022年3月期间，Antioquia大学医院120,354次住院记录。在清洁处理后，数据集被划分为训练集（67%）、测试集（22%）和验证集（11%）。训练了一个逻辑回归模型来预测pLoS，分为两个类别：小于或大于7天。模型的性能通过准确性、精确度、灵敏度、特异度和AUC-ROC指标进行评估。特征选择方法返回了九个可解释的变量，增强了模型的透明性。在验证集上，pLoS模型的特异度为0.83（95%CI，0.82-0.84），灵敏度为0.64（95%CI，0.62-0.65），准确性为0.76（95%CI，0.76-0.77），精确度为0.67（95%CI，0.66-0.69），AUC-ROC为0.82（95%CI，0.81-0.83）。该模型表现出强大的预测性能，并提供了影响延长住院时间的因素的见解。这使其成为医院管理和开发未来干预研究以减少pLoS的有价值的工具。
英文摘要：
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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