动态预测治疗失败的眼睛结核病使用机器学习和可解释的AI
William Rojas-Carabali1,2,3, Tristan Guérand4, Carlos Cifuentes-González2,3
1Centre for Biomedical Informatics, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
Translational vision science & technology
|October 24, 2025
概括
机器学习模型可以使用患者数据预测眼结核病治疗失败. 这种方法有助于及时进行干预,改善眼结核病 (OTB) 患者的治疗结果.
科学领域:
- 眼科医生 眼科 眼科
- 机器学习 机器学习
- 传染性疾病 传染性疾病
背景情况:
- 眼性结核病 (OTB) 带来了诊断和治疗方面的挑战.
- 预测治疗失败对于及时干预和更好的患者结果至关重要.
研究的目的:
- 应用机器学习 (ML) 来预测OTB治疗失败.
- 动态更新预测,使用患者病史和新的观察结果.
主要方法:
- 利用了来自协作眼部结核病研究 (COTS) 的数据,这是一项涉及836名患者的跨国回顾性研究.
- 评估了9个ML模型,包括XGBoost和随机森林 (RF),以预测6个,12个和24个月的治疗失败.
- 采用可解释性工具,如证据权重和特征重要性信息价值等.
主要成果:
- 在所有时间点上,XGBoost和RF模型在预测OTB治疗失败方面表现优异.
- 在6个月后,XGBoost实现了0.915的AUC和0.879.9的准确性.
- 在12个月和24个月后,RF显示出高性能,AUC分别为0.921和0.888,精度分别为0.944和0.960.
结论:
- ML模型,特别是XGBoost和RF,显示出对早期和准确预测OTB治疗失败的显著前景.
- 可解释性工具提高了这些ML模型的临床解释性.
- 这项研究弥合了ML和临床护理,支持OTB管理的数据知情治疗决策.
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