改善老年患者的护理过渡:一个大数据驱动的再接收预测模型,用于个性化的出院护理护理
Chan Lee1, Jina Kim1, Jinsol Son1
1Chungnam National University Hospital, 282 Munhwa-ro, Jung-gu, Daejeon 35015, Republic of Korea.
Geriatric nursing (New York, N.Y.)
|July 11, 2025
概括
这项研究使用机器学习确定了预测老年患者30天无计划再入院的关键因素. 开发的模型有助于护士识别有针对性的干预的高风险个体.
科学领域:
- 老年医学 老年医学
- 医疗信息学 医疗信息学
- 护理研究 护理研究
背景情况:
- 在老年人中,高的再入院率带来了重大管理挑战和成本.
- 识别意外再入院的预测因素对于改善患者的治疗结果和医疗保健效率至关重要.
研究的目的:
- 确定与65岁及以上患者30天无计划再入院相关的因素.
- 开发和验证基于机器学习的预测模型,用于这些再录取.
主要方法:
- 来自韩国一家三级普通医院的护理记录和电子医疗记录的分析 (2019年11月 - 2023年12月).
- 使用最小绝对收缩和选择运算符 (LASSO) 回归来进行变量选择,然后进行模型构建的后勤回归.
- 关键预测因素包括人口统计,入院细节,感染状况,药物,不良药物反应,韩国患者分类系统 (KPCS),查尔森并发症指数 (CCI),透析和化疗.
主要成果:
- 预测模型表现出适度的歧视性性能,曲线下的面积 (AUC) 为0.7484,准确度为0.8007,F1得分为0.8784.
- 确定了重要的预测因素,如年龄,性别,入院路线,感染,多药,药物不良反应,KPCS,CCI,透析和化疗.
结论:
- 开发的机器学习模型可以帮助护士识别高风险的老年患者进行30天的无计划再入院.
- 建议建立一个医院综合计划,用于早期识别和主动管理,以减少再入院和改善患者健康.
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