基于机器学习的预测,使用临床数据,在艾滋病毒感染者中预测活跃结核病
Lena Bartl1, Marius Zeeb1,2, Marisa Kälin1
1Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland.
概括
机器学习模型可以预测艾滋病毒感染者的活跃结核病,超过目前的测试. 这有助于提前识别,及时进行预防性治疗,这对于管理共感染至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 传染病流行病学 传染病流行病学
- 医疗保健中的机器学习
背景情况:
- 同时感染Mycobacterium结核病 (MTB) 和人类免疫缺陷病毒 (HIV) 是一个重大的全球卫生挑战.
- 患有MTB感染的个体患有活跃结核病 (TB) 的风险增加,这种进展可以通过及时治疗来预防.
- 现有的诊断方法往往无法识别具有随后活跃结核病发展高风险的个体.
研究的目的:
- 开发和验证机器学习模型,用于预测艾滋病毒感染者 (PWH) 发生的活性结核病.
- 评估这些模型的性能与标准诊断试验对结核病风险分层进行评估.
主要方法:
- 随机森林模型是使用从瑞士艾滋病毒队列研究 (SHCS) 定期收集的临床数据进行培训的.
- 培训数据集包括55名患有活跃结核病的PWH和1432名与没有结核病的PWH匹配.
- 外部验证是使用奥地利艾滋病毒队列研究 (AHIVCOS) 的数据进行的,包括43个病例性活跃结核病的PWH和1005个没有结核病的PWH.
主要成果:
- 该模型在SHCS.中实现了0.83的接收机运行特征 (ROC) 曲线 (AUC) 下的面积.
- 在调整和重新装配后,AUC值为0.72 (SHCS) 和0.67 (AHIVCOS) 得到.
- 机器学习模型与标准护理 (结核素皮肤测试和干扰素玛释放试验) 相比表现出更好的表现,诊断所需的数量更低 (1.96对4).
结论:
- 机器学习模型显示,通过改进可以从预防性结核病治疗中受益的个体的识别,提高PWH护理的巨大潜力.
- 这些模型不需要额外的数据收集,成本最小,提供了成本有效的解决方案.
- 这些发现支持将预测建模纳入临床实践,以更好地管理结核病/艾滋病毒共感染.
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