使用机器学习和可解释的人工智能方法预测生物药物的无效性:来自奥地利生物登记 BioReg 的数据
Dubravka Ukalovic1, Burkhard F Leeb2, Bernhard Rintelen3
1Siemens Healthcare GmbH, Computed Tomography, Forchheim, Germany. dubravka.ukalovic@siemens-healthineers.com.
Arthritis research & therapy
|February 8, 2024
概括
机器学习模型准确地预测了bDMARD的无效性,有助于个性化治疗. 患者数据揭示了影响药物反应的复杂相互作用,指导临床决策.
科学领域:
- 类风湿病学 类风湿病学
- 人工智能的人工智能
- 药理学 药理学是指药理学的学科.
背景情况:
- 生物修饰性抗风湿药物 (bDMARDs) 对于治疗炎症疾病至关重要.
- 个体患者对bDMARDs的反应有很大差异,需要个性化治疗策略.
- 预测bDMARD的无效性对于优化患者的治疗结果和医疗资源分配至关重要.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于预测五种不同的bDMARDs的无效性.
- 使用可解释AI (XAI) 识别影响bDMARD反应的关键患者特定因素.
- 为患者选择bDMARDs支持个性化的方法.
主要方法:
- 利用来自奥地利生物注册局 (BioReg) 的患者数据,包括1397名患者和19个变量.
- 训练了各种ML算法,在26周内预测bDMARD的无效性,使用交叉验证和超参数优化.
- 使用接收器操作特征 (AUROC) 曲线下的面积评估模型性能,并通过XAI提取影响因素.
主要成果:
- 为五个bDMARD开发了预测模型,AUROC得分从0.66到0.84.8不等.
- 确定了不有效性的关键预测因素,例如阿巴特阿塞普特/埃坦阿塞普特的视觉模拟量表 (VAS) 和阿达利姆巴的葡萄糖皮质激素联合治疗.
- 发现剂量对certollizumab至关重要,而性别和类风湿因子 (RF) 等因素在不同的bDMARD中具有不同的影响.
结论:
- 机器学习模型在预测bDMARD无效性方面显示出有希望的准确性.
- 患者特异性因素表现出复杂的,药物依赖的相互作用,影响治疗反应.
- ML和XAI可以帮助临床医生导航这些复杂的关系,以改善bDMARD选择.
相关概念视频
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