一种机器学习方法来探索Mukono地区结核病治疗不遵守的个体风险因素
Haron W Gichuhi1, Mark Magumba2, Manish Kumar3
1Department of Biostatistics and Epidemiology, Makerere University School of Public Health, Kampala, Uganda.
PLOS global public health
|July 3, 2023
概括
机器学习算法可以预测乌干达的结核病治疗不遵守情况. 识别有风险的患者有助于针对性干预,并改善结核病计划管理.
科学领域:
- 公共卫生 公共卫生
- 机器学习 机器学习
- 流行病学 流行病学
背景情况:
- 尽管有可用的干预措施,但结核病治疗的不遵守仍然是乌干达的一个挑战.
- 对于结核病计划来说,很难识别个别患有不服药风险的患者.
研究的目的:
- 探索机器学习方法来识别结核病治疗不遵守的个体风险因素.
- 评估五种分类算法在预测非依从性方面的表现.
主要方法:
- 一项回顾性研究审查了乌干达838名结核病患者的记录.
- 训练和评估了五种机器学习算法 (逻辑回归,人工神经网络,支持矢量机器,随机森林,AdaBoost).
- 性能指标包括准确性,F1得分,精度,回忆和AUC.
主要成果:
- 支持矢量机器 (SVM) 获得了最高的准确性 (91.28%),而AdaBoost在AUC (91.05%) 的基础上表现最好.
- 不坚持的关键预测因素包括结核病类型,GeneXpert结果,抗逆转录病毒状态,年龄,性别和唾液测试结果.
- 机器学习模型在区分坚持和不坚持患者方面表现出很高的准确性.
结论:
- 分类机器学习技术有效地识别了结核病治疗不坚持患者特异性风险因素.
- 这些方法可以作为结核病计划的有价值的查工具,有效地针对干预措施.
- 采用这些机器学习技术可以加强结核病计划管理,改善患者的治疗结果.
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