开发和验证一种机器学习模型,用于使用常规实验室数据对恶性肺溢液进行差异诊断
Ting-Ting Wei1, Jia-Feng Zhang1, Zhuo Cheng2
1Department of Laboratory Medicine, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China.
Therapeutic advances in respiratory disease
|November 9, 2023
概括
一个AI XGBoost模型使用常规实验室数据有效地区分恶性外流和良性外流. 这种机器学习方法为恶性多叶流血提供了更好的诊断准确性.
科学领域:
- 医学诊断 医学诊断 医学诊断
- 人工智能在医学中的应用
- 医疗保健中的机器学习
背景情况:
- 区分恶性流 (MPE) 和良性流 (BPE) 是一个临床挑战.
- 人工智能 (AI) 和机器学习 (ML) 模型越来越多地用于疾病诊断.
研究的目的:
- 开发和验证用于早期区分MPE和BPE的诊断模型.
- 用常规实验室数据来开发诊断模型.
主要方法:
- 一项追溯观察队列研究,涉及2352名患有多叶流血的患者.
- 使用81个实验室变量开发和比较五个ML模型 (XGBoost,LR,RF,SVM,MLP).
- 使用接收器操作特征 (ROC) 曲线评估诊断性能.
主要成果:
- 在训练,验证和测试队列中,XGBoost模型显示了MPE的最高诊断性能 (AUC:0.903,0.918,0.886).
- 在区分MPE和BPE方面,XGBoost的表现优于癌胚抗原 (CEA).
- 与CEA相比,XGBoost模型对肺癌诱导的MPE具有更高的诊断能力和灵敏度.
结论:
- 使用常规实验室生物标志物的机器学习模型显著提高了区分MPE和BPE的诊断能力.
- XGBoost模型是诊断恶性多流的有价值的工具.
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