CD-Tron:利用大型临床语言模型从电子健康记录中早期发现认知衰退
Hao Guan1, John Novoa-Laurentiev2, Li Zhou1
1Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA.
Journal of biomedical informatics
|May 4, 2025
概括
这项研究介绍了CD-Tron,一种新的AI模型,使用临床笔记来早期检测认知衰退. CD-Tron显著提高了鉴定临床前阿尔茨海默病和相关痴呆症的准确性,有助于及时干预.
科学领域:
- 医疗保健中的人工智能
- 临床数据的自然语言处理.
- 神经退行性疾病研究研究
背景情况:
- 早期发现认知衰退 (CD) 对管理阿尔茨海默病和相关痴呆症 (AD/ADRD) 至关重要.
- 电子健康记录 (EHR) 包含有价值的非结构化数据,用于识别临床前CD.
- 大规模的临床语言模型为提升早期CD检测提供了潜力.
研究的目的:
- 开发和评估CD-Tron,一个大型临床语言模型,以使用EHR临床笔记改进认知衰退的早期检测.
- 评估模型在真实世界的临床数据上的表现及其识别关键预测特征的能力.
主要方法:
- 在轻度认知障碍 (MCI) 诊断后四年内收集了2,166名患者的临床笔记.
- 通过对4949个专家标记的临床笔记部分微调一个大型临床语言模型来开发CD-Tron.
- 在 1,996 个独立的注释部分评估了 CD-Tron,并使用 SHAP 值来解释.
主要成果:
- 与CD检测的基线模型相比,CD-Tron在精度,回忆和AUC方面显著改善.
- 该模型在真实世界的临床笔记上只有一次假负结果,实现了高灵敏度.
- SHAP分析确定了关键的文本特征,提高了模型透明度和临床医生的信任.
结论:
- CD-Tron提供了一种新的,准确的方法,用于使用自由文本EHR数据早期检测认知衰退.
- 该模型的SHAP值集成提高了预测解释性和临床实用性.
- 这种方法支持对患有或有ADRD风险的患者及时进行干预.
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