自然语言处理系统用于快速检测和干预心理健康危机聊天消息
Akshay Swaminathan1,2, Iván López3,4, Rafael Antonio Garcia Mar5,6
1Cerebral Inc, Claymont, DE, USA. akshay325@gmail.com.
NPJ digital medicine
|November 22, 2023
概括
一个机器学习系统显著减少了远程医疗中心理健康危机信息的响应时间. 这种自然语言处理 (NLP) 工具有助于识别紧急情况,改善患者的护理和安全.
科学领域:
- 人工智能的人工智能
- 临床信息学 临床信息学
- 心理健康技术 心理健康技术
背景情况:
- 患有心理健康危机的患者经常使用消息平台,因为有限的分拣能力而面临延误.
- 对于经历急性痛苦的个人来说,如自杀念头或家庭暴力,快速反应至关重要.
研究的目的:
- 开发和实施一个支持机器学习 (ML) 的系统,以加快与危机有关的消息的分类.
- 通过远程医疗改善寻求紧急心理健康支持的患者的响应时间.
主要方法:
- 在电子医疗记录聊天消息上使用关键字过和后勤回归来训练一个双阶段自然语言处理 (NLP) 系统.
- 该模型在回顾性和前性数据集上进行了评估,包括前性测试集中的超过10万条消息.
主要成果:
- NLP模型表现出高性能,在前性测试组中AUC为0.98.
- 在前性评估中,灵敏度达到0.98和正预测值 (PPV) 为0.66.
- 该系统将选时间的中位数从9小时减少到8-13分钟.
结论:
- 基于NLP的ML模型可以在远程医疗环境中有效地识别潜在的危机聊天消息.
- 该系统可以集成到现有的工作流程中,使临床医生能够利用ML进行高效的危机信息分类.
- 这种方法提高了患者的安全性和为急需心理健康的个人提供护理.
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