使用机器学习和纵向真实世界数据,识别和描述高风险严重恶化喘子组
Andres Quintero1, Javier Lopez-Molina2, Merina Su2
1Pfizer, New York, New York, USA ajquintero.ads@gmail.com.
BMJ health & care informatics
|July 7, 2025
概括
这项研究使用机器学习来识别5个不同的喘患者分组,这些患者经历严重急性恶化 (AEs). 这些发现突显了患者的异质性和需要个性化喘管理策略.
科学领域:
- 计算生物学和生物信息学
- 呼吸系统药物 呼吸系统药物
- 在医疗保健中的数据科学.
背景情况:
- 喘急性恶化 (AE) 对患者和医疗保健系统构成重大负担.
- 了解患有喘患者群体中的异质性,对于有效管理至关重要.
研究的目的:
- 通过多步式机器学习方法,识别和描述具有严重急性恶化 (AE) 的喘患者的不同亚组.
- 利用监督和无监督学习来发现新的患者表型.
主要方法:
- 利用美国全付款医疗和处方索赔数据 (2015年10月至2022年5月) 对一大批喘患者.
- 在AE预测中使用渐变增强的决策树,导出SHapley添加式扩展 (SHAP) 值.
- 应用非线性维度缩小和基于密度的聚类到SHAP值以识别患者子组.
主要成果:
- 确定了5个不同的喘患者的亚组.
- 分组根据临床因素进行了区分,包括急性护理史,医疗保健利用,治疗,喘严重程度,专家访问,吸烟和情绪障碍.
- 在子组中观察到AE预测概率的显著变化,一些集群对标准预测模型构成挑战.
结论:
- 这项研究揭示了喘患者经历副作用的显著异质性,强调了个性化管理的必要性.
- 机器学习应用于现实世界的数据可以识别离散的患者表型,告知风险评估和缓解策略.
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