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开发一种机器学习建模工具,用于使用美国南部一个县的公共卫生数据来预测艾滋病毒发病率

Carlos S Saldana¹, Elizabeth Burkhardt², Alfred Pennisi²

¹Division of Infectious Diseases, Department of Medicine, Emory University School of Medicine, Atlanta, Georgia, USA.

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America

|February 23, 2024

概括

机器学习模型使用性传播感染 (STD) 的公共卫生数据准确预测人类免疫缺陷病毒 (HIV) 发病率. 这种方法可以提高艾滋病毒检测和预防战略在高发病率地区.

科学领域:

公共卫生公共卫生
流行病学流行病学
机器学习机器学习

背景情况:

机器学习 (ML) 模型在预测人类免疫缺陷病毒 (HIV) 发病率方面表现有前途.
目前的模型通常依赖于电子医疗记录和登记册.
本研究探讨使用未识别的性传播感染 (STIs) 公共卫生数据集来预测艾滋病毒发病率.

研究的目的:

评估ML在预测艾滋病毒发病率方面的可行性和准确性.
扩大使用公共卫生 STI 数据的 ML 工具的应用.
为预防艾滋病毒提供信息并加强公共卫生干预措施.

主要方法:

对可报告性性传播感染的非识别性公共卫生数据集的分析 (2010年1月至2021年12月).
包括社会人口因素,性病例和社会脆弱性指数 (SVI) 的指标.
训练和评估各种ML模型使用准确性,精度,回忆和F1分数.

主要成果:

包括85,224个人;2.37%被新诊断为艾滋病毒.
ML模型在预测男性和女性HIV发病率方面表现出很高的表现.
关键预测因素包括年龄,以前的性病,提供者类型和SVI指标.

结论:

机器学习模型的高准确性表明,有潜在的公共卫生干预措施,如量身定制的艾滋病毒检测和预防.
公共卫生 STI 数据集对于预测艾滋病毒发病率非常有价值.
需要进一步的研究来将ML模型转化为实际的公共卫生应用.

关键词:

啊啊啊啊啊啊啊啊啊啊啊啊啊啊啊艾滋病病毒艾滋病病毒艾滋病病毒人工智能是一种人工智能.机器学习是机器学习.公共卫生公共卫生.

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