可靠的数据和人工智能环境用于临床预测:应用于抑郁症患者的危机风险
IEEE journal of biomedical and health informatics
|September 5, 2023
概括
在抑郁症患者中预测心理健康危机是可行的,使用电子健康记录 (EHR). 机器学习模型,特别是LSTM网络,通过分析结构化和非结构化EHR数据,准确预测危机事件.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 心理健康研究 心理健康研究
背景情况:
- 抑郁症经常与慢性疾病同时发生,严重程度各不相同.
- 电子健康记录 (EHR) 为开发预测模型提供了全面的患者数据.
- 将预测工具集成到临床实践中可以增强患者护理并支持精神卫生保健服务.
研究的目的:
- 评估预测抑郁症患者心理健康危机的系统的可行性.
- 为了利用结构化和非结构化EHR数据进行危机预测.
- 在这个预测任务中评估各种机器学习模型的性能.
主要方法:
- 使用了来自Mersey Care的大型,匿名的EHR数据集.
- 使用CogStack平台进行数据摄入和自然语言处理 (NLP).
- 使用MedCAT和BioYODIE NLP服务进行应用语义注释.
- 训练并比较了随机森林,梯度增强和长短期记忆 (LSTM) 网络模型.
- 使用特征选择和时间性考虑的优化模型.
主要成果:
- 所有测试的模型都在使用组合的电子健康记录数据预测危机时表现出很好的准确性.
- 使用1000个关键功能和时间数据的LSTM网络实现了最高的性能.
- 在训练数据上达到0.901 (SD 0.006) 的平均AUC,在测试数据上达到0.810 (SD 0.01).
- 技术发现与精神病学家对潜在的临床整合的看法一致.
结论:
- 使用结构化和非结构化EHR数据的预测模型对于识别与抑郁相关的心理健康危机是有效的.
- LSTM模型对现实世界的临床决策支持有显著的前景.
- 有一个明显的机会将这些先进的预测工具集成到关心点的精神卫生保健环境中.
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