使用LGBM和SHAP进行结核性多支流的可解释的非侵入性诊断:开发和临床应用机器学习模型
Bihua Yao1, Xingyu Yu2, Liannv Qiu3
1Laboratory Medicine Center, Department of Clinical Laboratory, The First People's Hospital of Jiashan Affiliated to Jiaxing University, Jiaxing, Zhejiang, China.
PeerJ
|May 26, 2025
概括
这项研究开发了一种可解释的机器学习模型,用于非侵入性地诊断结核性多叶流血 (TPE). 该模型使用常规实验室数据,实现高准确性并帮助早期临床决策.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习在医学中的应用
- 肺部病理学 肺部病理学
背景情况:
- 结核性流 (TPE) 是一种常见的结核病并发症,需要准确的诊断.
- 目前对TPE的诊断方法往往是侵入性的,耗时的,缺乏精度.
- 对于TPE而言,非常需要非侵入性和快速诊断工具.
研究的目的:
- 设计和验证可解释的机器学习 (ML) 模型,用于非侵入性TPE诊断.
- 利用常规实验室数据进行快速准确的TPE识别.
- 通过早期检测TPE来改善患者的管理和预后.
主要方法:
- 一项多中心前性研究在中国招募了963名患者.
- 使用18个常规实验室参数开发了一个光梯度增强机 (LGBM) 模型.
- 采用沙普利添加式解释 (SHAP) 分析进行模型解释性和特征重要性评估.
主要成果:
- 该LGBM模型实现了高性能,AUC为0.9454 (内部) 和0.9262 (外部验证).
- SHAP分析确定了11个关键变量,并增强了模型的解释性.
- 该模型在外部验证中显示出强大的通用性,灵敏度为0.8600,特异性为0.9056.
结论:
- 一个可解释的LGBM模型为TPE诊断提供了一种非侵入性,准确的方法,减少了对侵入性手术的需求.
- 整合SHAP解决了"黑子"问题,确保了临床解释性.
- 该模型支持临床决策,并有可能集成到临床信息系统中.
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