性结肠炎综合征的预测模型与集体学习和可解释性方法.
Ling Zhu1, Shan He2, Wanting Zheng3
1Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China. jjzhuling@163.com.
Scientific reports
|July 1, 2025
概括
这项研究介绍了传统中医 (TCM) 综合征在性结肠炎 (UC) 中的差异化. 使用SHAP和LIME组合模型可以提高准确性,并为更好的患者结果提供临床见解.
科学领域:
- 综合医学是一个整体医学.
- 医疗保健中的人工智能
- 传统中国医学 (TCM) 是一种
背景情况:
- 性结肠炎 (UC) 等慢性疾病的患病率不断上升,给医疗保健带来了重大负担.
- 传统中医 (TCM) 为UC提供了具有成本效益和高效的治疗选择.
- 在TCM中对UC的综合征差异化是具有挑战性的,原因是慢性和多样化的症状,在AI模型中对可解释性的研究有限.
研究的目的:
- 开发和评估一个可解释的集体预测模型,用于UC中TCM综合征的分化.
- 提高人工智能模型在TCM实践中的解释性和临床实用性.
- 在TCM框架内确定导致不同UC综合征的关键特征.
主要方法:
- 利用了来自东方医院 (2006-2019) 8078 个电子病历的数据集.
- 开发了一个集体预测模型,包括SHAP (夏普利增量解释) 和LIME (局部可解释模型-不可知解释).
- 对个体深度学习方法的模型性能进行了评估,并分析了综合征差异化的特征重要性.
主要成果:
- 整体模型的表现优于个别的深度学习方法.
- 梯度提升 (GB) 模型在综合征差异化方面获得了83%的F1得分.
- SHAP和LIME确定了关键的区分特征,例如频繁的便 (脏缺陷) 和下腹寒冷 (脏缺陷).
结论:
- 提出的可解释的人工智能模型显著改善了UC的TCM综合征分化.
- SHAP和LIME提供了宝贵的临床见解,有助于智能综合征的分化和决策.
- 这些发现支持基于TCM的UC管理的进步,并改善了患者的治疗结果.
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