增强自杀企图风险预测模型与时间临床注释特征的提升
Kevin J Krause1, Sharon E Davis1, Zhijun Yin1
1Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States.
Applied clinical informatics
|September 9, 2024
概括
从临床笔记中用时间概念独特标识符 (CUI) 增强自杀企图风险模型显著提高了预测准确性. 混合模型,特别是窗口时间化的LSTM,在识别有风险的个体方面表现出卓越的表现.
科学领域:
- 医疗信息学 医疗信息学
- 临床数据科学 临床数据科学
- 计算精神病学是一种计算精神病学.
背景情况:
- 准确的自杀企图风险预测对于及时干预至关重要.
- 结构化电子健康记录 (EHR) 数据本身在捕捉复杂的患者风险因素方面存在局限性.
- 整合非结构化的临床笔记可以提供对患者风险更丰富的见解.
研究的目的:
- 从临床笔记中增强基于结构化数据的自杀企图风险预测模型,使用时间概念独特标识符 (CUI).
- 评估不同时间化方案和模型类型对预测性能的影响.
- 确定非结构化临床数据在改善自杀风险预测方面的价值.
主要方法:
- 使用诊断代码对在30,90或365天内自杀的尝试.
- 仅使用结构化EHR数据 (药物,诊断,人口统计) 的模型与包含非结构化文本数据的混合模型进行比较.
- 雇佣了固定的90天窗口和灵活的时代来定时临床笔记.
- 训练和评估随机森林和混合长短期记忆 (LSTM) 神经网络.
主要成果:
- 从临床笔记中结合时间CUI的模型表现优于仅基于结构化数据的模型.
- 窗口时间化的LSTM模型在30天的预测中实现了最高的精度回忆曲线下的区域 (AUPRC).
- 与控制模型相比,混合模型通常在各种指标上表现得更好.
结论:
- 纳入EHR衍生的临床笔记特征显著提高了自杀企图风险预测模型.
- 非结构化的临床数据在提高自杀性预测的准确性方面发挥着至关重要的作用.
- 未来的研究应该探索先进的方法,以进一步完善预测准确性和干预有效性.
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