深度学习模型可以使用临床笔记预测针对医疗保健提供者的暴力和威胁
Nicholas J Dobbins1,2, Jacqueline Chipkin3, Tim Byrne4
1Biomedical Informatics & Data Science, Johns Hopkins University, Baltimore, MD, USA. nic.dobbins@jhu.edu.
通过分析临床笔记的深度学习模型,可以预测患者对医疗保健工作者的暴力行为. 这些模型显著优于人类专家,为早期干预提供了一个新的工具,并提高了员工的安全.
科学领域:
- 医疗保健管理的管理
- 人工智能在医学中的应用
- 临床信息学 临床信息学
背景情况:
- 患者对医疗服务提供者的暴力在全球范围内带来了重大挑战.
- 它有助于员工流动,痛苦和工作满意度降低.
- 需要积极的干预措施来减轻这些风险.
研究的目的:
- 开发深度学习模型来预测患者对医疗保健工作者的暴力行为.
- 将模型性能与人类专家预测进行比较.
- 识别与暴力事件相关的风险因素.
主要方法:
- 训练了两个深度学习模型:文档分类 (临床笔记) 和回归 (结构化数据).
- 使用命名实体识别 (NER) 分类器来识别风险因素.
- 评估模型使用F1得分与精神病学团队的表现对比.
主要成果:
- 文档分类模型获得了F1得分0.75.75.
- 结构化数据模型实现了F1得分为0.72.
- NER分类器获得了0.7的整体F1,超过了人类基线 (0.5F1).
结论:
- 深度学习模型可以有效地预测医疗保健环境中的患者暴力.
- 临床笔记分析显示出卓越的预测性能.
- 这些方法提供了广泛实施的潜力,以提高安全.
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