通过临床文档的自然语言处理来识别痴呆症患者的功能状态障碍:跨部分研究
John Laurentiev1, Dae Hyun Kim1,2,3, Mufaddal Mahesri1
1Department of Medicine, Brigham and Women's Hospital, Boston, MA, United States.
Journal of medical Internet research
|February 13, 2024
概括
机器学习模型从痴呆症患者的临床笔记中准确识别日常生活障碍. 这种方法通过有效地提取关键的日常生活活动 (ADL) 和仪器ADL (iADL) 数据来改善痴呆症严重性评估.
科学领域:
- 老年学是一门学科.
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 评估日常生活活动 (ADL) 和仪器ADL (iADL) 对老年人痴呆症严重程度和护理规划至关重要.
- 关于ADL/iADL的信息通常嵌入于非结构化的自由文本临床笔记中,这给可访问性带来了挑战.
- 开发自动化方法来提取这些数据对于高效的临床实践至关重要.
研究的目的:
- 开发和验证用于识别ADL和iADL损伤的机器学习 (ML) 模型.
- 使用电子健康记录 (EHR) 的自由文本临床笔记进行这种自动化评估.
- 加强对痴呆症严重程度和护理需求的确定.
主要方法:
- 一项采用EHR笔记和Medicare索赔数据 (2007-2017) 的横截面研究,针对65岁以上痴呆症诊断的个人.
- 模型在痴呆症诊断日期之前和之后采集的笔记上进行了训练和验证.
- 使用接收器操作特征曲线 (AUROC) 下的区域和精度回忆曲线 (AUPRC) 下的区域来评估性能,比较关键字过和未过的笔记队列.
主要成果:
- 在两个队列中,ADL和iADL模型都实现了高AUROC值 (>0.97).
- 表现最好的模型在ADL (过) 中达到0.89的AUPRC,在ADL (未过) 中达到0.82.
- 对于iADL,Bio+Clinical BERT模型实现了AUPRC的0.76 (过) 和0.58 (未过),与关键字搜索相比,显著降低了假阳性率.
结论:
- 机器学习模型可以从临床笔记中准确识别ADL和iADL损伤.
- 这种能力可以显著帮助评估痴呆症的严重程度.
- 这些发现表明,它是改善痴呆症护理管理的宝贵工具.
关键词:
这些ADLs是ADLs.欧洲人权理事会 欧洲人权理事会在NLP中,我们使用了NLP.日常生活中的日常生活活动.临床注意事项 临床注意事项痴呆症 痴呆症是一种痴呆症.电子健康记录 电子健康记录功能障碍 功能障碍 功能障碍在iADLs中使用.日常生活中的工具性活动.机器学习是机器学习.自然语言处理自然语言处理.更多相关视频
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