精神病医院住院后与情绪或精神病障碍的再入院的跨站点预测:回顾性研究
Boyu Ren1,2, WonJin Yoon3,4, Spencer Thomas3
1Laboratory for Psychiatric Biostatistics, McLean Hospital, Belmont, MA, United States.
JMIR mental health
|September 12, 2025
概括
预测精神病医院再入院需要特定的场所模型,因为不同的患者因素. 目前的电子健康记录数据对于跨站点预测模型是不够的,需要先进的算法.
科学领域:
- 精神病医疗服务研究 精神病医疗服务研究
- 医疗信息学 医疗信息学
- 临床预测建模临床预测建模
背景情况:
- 患有情绪或精神疾病的患者有较高的非计划性再入院率.
- 准确预测再接收风险可以改善患者护理,并指导出院规划.
研究的目的:
- 评估电子健康记录 (EHR) 数据在多个医疗保健站点的各种原因再入院的预测准确性.
- 评估预测模型在不同临床场所之间的通用性和可移植性.
主要方法:
- 一个回顾性的多站点研究,使用结构化的EHR变量.
- 在每个地点开发现场预测模型,用于每个地点的再接收.
- 通过跨站点应用模型来评估模型的可运输性,使用F1得分来测量性能.
主要成果:
- 在不同地点的再接收预测指标中观察到显著的差异.
- 现场模型平均F1得分为0.661,超过跨站点模型 (平均F1得分为0.616).
- 对预测分布差异的调整并没有提高跨站点模型的可转移性.
结论:
- 特定地点的预测模型对于准确的再接收风险评估至关重要.
- 目前的EHR预测器不足以开发可运输的跨站点模型.
- 未来的研究应该专注于先进的预测变量和算法,以改善精神病学再接收预测.
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