FANS:基于自由文本临床笔记的营养状况自动评估框架
Jiahui Hu1, Xue Wang2, Kuanda Yao1
1Institute of Medical Information/Medical Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
International journal of medical informatics
|October 31, 2025
概括
这项研究开发了一种自然语言处理 (NLP) 方法,用于自动识别住院患者的营养不良风险因素. 这种方法可以快速评估营养状况,改善临床决策在有限的数据.
科学领域:
- 医疗保健中的人工智能
- 临床信息学 临床信息学
- 自然语言处理 (NLP) 是一种自然语言处理.
背景情况:
- 营养不良在住院患者中很普遍,影响了结果和成本.
- 目前的营养状况评估是手动的,耗时的,劳动密集的.
- 机器学习 (ML) 是有前途的,但由于临床数据有限而面临挑战.
研究的目的:
- 从临床笔记中开发和验证一种自动化的方法来识别营养不良的风险因素.
- 使用有限的标记数据快速评估住院患者的营养状况.
- 利用NLP和ML进行高效的营养风险评估.
主要方法:
- 根据GLIM,SGA和ESPEN指导方针,收集并注释了来自1469名患者的临床笔记.
- 开发了一个自动评估营养状况 (FANS) 的框架,其中包括风险因素识别和状态评估组件.
- 采用NLP模型来提取风险因素和半监督学习 (SSL),使用自学算法 (SLA) 进行状态评估,使用SHAP进行解释性.
主要成果:
- 预先训练的NLP模型在风险因素识别方面获得了0.9142的F1得分,超过了传统的ML模型.
- 基于SLA的SSL提高了性能,在营养状况评估中获得了0.8234的AUROC.
- SHAP分析确定了无意减肥和减少食物摄入量是关键风险因素.
结论:
- 介绍了一种可行的基于NLP的方法,用于医院的自动营养风险评估.
- 证明了ML和NLP在高性能医疗保健AI服务中的潜力.
- 强调了营养状况评估自动化系统的效率和准确性.
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