使用结构化和非结构化数据预测住院出院后青少年的自杀行为.
Nicholas J Carson1, Xinyu Yang2, Brian Mullin1
1Health Equity Research Lab, Cambridge Health Alliance, 1035 Cambridge Street, Cambridge, MA 02139, USA.
Journal of affective disorders
|December 29, 2023
概括
使用电子健康记录的新算法可以在精神病医院出院三个月内预测与青少年自杀相关的ICD代码. 这个工具有助于评估自杀风险,以更好地照顾患者.
科学领域:
- * 临床信息学 * 临床信息学
- * 医疗保健中的机器学习
- *青少年精神病学 青少年精神病学
背景情况:
- * 开发自杀风险的预测算法对于及时干预至关重要.
- *使用电子健康记录 (EHR) 数据为识别有风险的个体提供了丰富的来源.
研究的目的:
- * 创建和评估用于预测与自杀相关的国际疾病分类 (ICD) 代码的算法.
- * 评估算法在识别精神病医院出院后具有自杀行为高风险的年轻人的表现.
主要方法:
- * 对来自美国东北部安全网机构的2789名青少年 (12-20岁) 进行了回顾性队列研究.
- * 结合结构化EHR数据与通过自然语言处理处理的非结构化临床笔记.
- * 机器学习模型的比较,包括梯度增强和随机森林分析.
主要成果:
- * 梯度增强模型实现了ROC曲线下的面积为0.88.
- * 该模型显示了80%的灵敏度和76%的特异性,最佳切断概率为0.009.9.
- * 算法在测试组中正确识别了10个阳性病例中的8个.
结论:
- * 整合结构化和非结构化EHR数据可以改善自杀行为预测算法.
- * 开发的算法显示了临床整合到精神病学服务的前景,以提高自杀风险评估.
- *建议在多个医疗保健系统进行进一步验证,以减轻潜在的偏见.
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