机器学习决策支持模型用于中风患者的出院计划.
Yanli Cui1,2, Lijun Xiang1, Peng Zhao1,2
1Department of Neurology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Journal of clinical nursing
|February 15, 2024
概括
机器学习模型可以预测中风患者的出院需求. 关键因素包括NIHSS得分,收入和脆弱性,有助于及时做出临床决策,以获得更好的患者护理.
科学领域:
- 神经学 神经学
- 医疗信息学 医疗信息学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 对中风患者进行有效的出院计划对于康复和资源管理至关重要.
- 早期识别需要非家庭出院的患者是具有挑战性的,但对于最佳的护理过渡至关重要.
研究的目的:
- 利用入院后24小时内可用的数据,开发中风患者出院处置的早期预测模型.
- 确定关键的患者特征和影响出院计划的变量.
主要方法:
- 一项前性观察性研究,涉及523名大学医院中风患者.
- 开发和评估六种机器学习模型,以预测家庭和非家庭退学情况.
- 分析特征的重要性,以确定重要的预测因素.
主要成果:
- 性能最好的机器学习模型实现了0.95的AUC,用于预测非家庭出院.
- 最重要的预测因素包括国家卫生研究院中风量表 (NIHSS) 评分,家庭收入,巴特尔指数 (BI) 评分,FRAIL评分,跌倒风险,压力损伤风险,养方法,抑郁症,年龄和消化不良.
- 30.01%的中风患者有非家庭出院.
结论:
- 机器学习模型可以有效地预测中风患者的非家庭出院需求.
- 诸如较高的NIHSS,BI,FRAIL分数,家庭收入,跌倒风险,压力损伤风险,年龄较大,输液管养,抑郁症和消化不良等因素是强有力的预测因素.
- 这些模型支持及时的临床决策,并可以改善中风幸存者的出院过程.
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