人工智能方法优化住院痴呆患者的安全性
Lauren Bangerter1, Allan Fong2, Garrett Zabala3
1Health Economics and Aging Research (HEAR) Institute, Medstar Health Research Institute, Columbia, MD, USA lauren.r.bangerter@medstar.net.
BMJ open quality
|September 3, 2025
概括
机器学习模型可以识别导致痴呆相关安全事件的因素. 这有助于通过有针对性的干预改善住院痴呆患者的护理.
科学领域:
- 医疗信息学
- 患者安全
- 机器学习应用
背景情况:
- 与痴呆相关的安全事件在医院环境中存在重大风险.
- 确定这些事件的根本原因对于改善患者护理和安全至关重要.
- 现有的分析安全事件报告的方法可以通过先进的分析技术来增强.
研究的目的:
- 开发机器学习 (ML) 模型,以识别导致痴呆相关安全事件的因素.
- 使用患者安全事件报告数据来发现这些事件的模式和驱动因素.
- 增强对痴呆症护理中的情境因素和主动失败的理解.
主要方法:
- 利用美国卫生系统的1387份与痴呆相关的安全事件报告.
- 使用约克郡贡献因素框架来编码自由文本报告数据.
- 开发了两个eXtreme渐变增强 (XGBoost) ML模型来分类病人的情境因素和主动失败.
主要成果:
- 局势因素的ML模型实现了高性能,F1得分为0.834和ROC AUC为0.833.
- 活动故障模型显示强特异性 (0.992) 和ROC AUC为0.817,尽管精度和回忆率较低.
- 报告中很大一部分 (71.3%) 与局势因素有关,而积极失败占8.6%.
结论:
- 机器学习模型有效地识别与痴呆症安全事件相关的情境因素和主动故障.
- 这些发现支持有针对性的干预措施,包括员工进行行为症状管理的培训.
- 建议药剂师主导药物优化,以提高住院痴呆患者的安全性.
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