评估机器学习模型的开发,用于预测第三级总医院住院患者的住院时间
1Inha University Hospital, 27 Inhang-ro, Jung-gu, Incheon 22332, Republic of Korea.
Asian nursing research
|November 14, 2025
概括
机器学习使用临床和护理数据准确预测住院时间. 这有助于资源配置和患者护理计划.
科学领域:
- 医疗保健信息学 医疗保健信息学
- 预测分析是一种预测分析.
- 临床决策支持 临床决策支持
背景情况:
- 优化医院资源配置对于效率至关重要.
- 以患者为中心的护理和护理工作流程需要有效的管理.
- 预测建模可以提高医疗保健系统的性能.
研究的目的:
- 开发一个机器学习模型来预测住院时间.
- 纳入临床,护理和医疗保健系统的因素.
- 提高资源配置,患者护理和护理效率.
主要方法:
- 住院患者电子病历的回顾性分析.
- 机器学习算法的开发和评估.
- 使用特征重要性识别显著的预测变量.
主要成果:
- 随机森林算法在预测停留时间方面取得了最高的准确性.
- 关键预测因素包括咨询,ICU停留,呼吸机使用和感染隔离.
- 诸如跌倒风险和压力风险等护理因素与更长的停留时间相关.
结论:
- 机器学习模型有效地预测住院时间.
- 支持医院资源管理,护理分配和患者安全.
- 预测分析可以提高风险评估,出院计划和医院效率.
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