使用机器学习模型预测慢性阻塞性肺病患者对吸入性皮质类固醇维持治疗的反应
Shan-Chieh Wu1, Chih-Ying Wu2, Jung-Yien Chien3
1Graduate Institute of Clinical Pharmacy, College of Medicine, National Taiwan University, Taipei, Taiwan; Department of Pharmacy, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan.
Respiratory medicine
|September 27, 2025
概括
机器学习确定了慢性阻塞性肺病 (COPD) 患者吸入性皮质类固醇 (ICS) 反应的关键预测因子. 这种方法可能有助于个性化COPD治疗策略,以获得更好的结果.
科学领域:
- 肺部医学 肺部医学
- 在医疗保健中的数据科学.
- 药物基因组学 药物基因组学
背景情况:
- 在慢性阻塞性肺病 (COPD) 中,吸入性皮质类固醇 (ICS) 的有效性各不相同.
- 已知但不够的预测因素,如血中乙氨基酸细胞计数和恶化史.
- 治疗反应的异质性需要先进的预测模型.
研究的目的:
- 开发一种机器学习 (ML) 模型,用于预测COPD中的ICS反应.
- 识别超出已建立标记的ICS有效性的新型预测因素.
- 根据个人化治疗的恶化风险对COPD患者进行分层.
主要方法:
- 利用全国范围的行政数据库与COPD患者启动ICS的实验室结果联系在一起.
- 开发了使用后勤回归,拉索回归和极端梯度增强 (XGBoost) 的预测模型.
- 通过ROC曲线评估模型性能,并通过Shapley添加式解释确定关键预测因素.
主要成果:
- XGBoost模型在预测ICS反应方面表现出卓越的区分能力.
- 年龄较小,男性性别,并发性喘,以及较低之前的COPD药物使用率预测了有利的ICS反应.
- 血中乙氨基的升高具有有限的预测价值;恶化史的影响因风险组而异.
结论:
- 机器学习模型可以识别COPD中ICS反应的显著预测因素.
- 这些发现支持为COPD患者开发个性化治疗策略.
- 风险分层和新型预测因素可以提高吸入性皮质类固醇治疗的有效性.
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