Business School, Sichuan University, Chengdu 610065, China.
West China Hospital, Sichuan University, Chengdu 610041, China.
J Healthc Eng. 2019 Jan 27;2019:4571636. doi: 10.1155/2019/4571636. eCollection 2019.
The vast majority of patients with intracerebral hemorrhage (ICH) suffer from long and uncertain length of stay (LOS). The aim of our study was to provide decision support for discharge and admission plans by predicting ICH patients' LOS probability distribution. The demographics, clinical predictors, admission diagnosis, and surgery information from 3,600 ICH patients were used in this study. We used univariable Cox analysis, multivariable Cox analysis, Cox-variable of importance (Cox-VIMP) analysis, and an intersection analysis to select predictors and used random survival forests (RSF)-a method in survival analysis-to predict LOS probability distribution. The Cox-VIMP method constructed by us effectively selected significant correlation predictors. The Cox-VIMP RSF model can improve prediction performance and is significantly different from the other models. The Cox-VIMP can contribute to the screening of predictors, and the RSF model can be established through those predictors to predict the probability distribution of LOS in each patient.
绝大多数脑出血（ICH）患者的住院时间（LOS）较长且不确定。我们的研究旨在通过预测 ICH 患者的 LOS 概率分布为出院和入院计划提供决策支持。本研究使用了 3600 名 ICH 患者的人口统计学、临床预测因素、入院诊断和手术信息。我们使用单变量 Cox 分析、多变量 Cox 分析、Cox 变量重要性（Cox-VIMP）分析和交集分析来选择预测因子，并使用随机生存森林（RSF）-一种生存分析方法-来预测 LOS 概率分布。我们构建的 Cox-VIMP 方法有效地选择了显著相关的预测因子。Cox-VIMP 的 RSF 模型可以提高预测性能，并且与其他模型有显著差异。Cox-VIMP 有助于筛选预测因子，而 RSF 模型可以通过这些预测因子建立，以预测每个患者的 LOS 概率分布。
