TY - JOUR AU - Xu, AU - Yu, Zhen AU - Ge, AU - Chow宗元，Eric P F AU - Bao, Yining AU - Ong, Jason J AU - Li, Wei AU - Wu, Jinrong AU - Fairley, Christopher K AU - Zhang, Lei PY - 2022 DA - 2022/8/25 TI -基于机器学习算法的个人HIV和性传播感染风险预测工具:互联网发展和外部验证研究乔- J地中海Res SP - e37850六世24 - 8 KW - HIV KW -性传播感染KW -梅毒KW -淋病KW -衣原体KW -性健康KW -千瓦性传播性传播KW -预测KW -网络KW -风险评估KW -机器学习千瓦模型KW -算法KW -预测KW -风险KW -发展KW -验证AB -背景:艾滋病毒和性传播感染是全球重大公共卫生问题。在全世界15岁至49岁的人群中，每天发生100多万可治愈的性传播感染。检测或筛查不足严重阻碍了消除艾滋病毒和性传播感染。目的:我们研究的目的是开发一种使用机器学习算法的HIV和STI风险预测工具。方法:我们使用2015年3月2日至2018年12月31日期间在墨尔本性健康中心检测艾滋病毒和性传播感染的临床咨询作为开发数据集(培训和测试数据集)。我们还使用了2个外部验证数据集，包括2019年的数据作为外部“验证数据1”，2020年1月和2021年1月的数据作为外部“验证数据2”。我们开发了34个机器学习模型来评估感染艾滋病毒、梅毒、淋病和衣原体的风险。我们创建了一个在线工具来了解个人感染艾滋病毒或性传播感染的风险。 Results: The important predictors for HIV and STI risk were gender, age, men who reported having sex with men, number of casual sexual partners, and condom use. Our machine learning–based risk prediction tool, named MySTIRisk, performed at an acceptable or excellent level on testing data sets (area under the curve [AUC] for HIV=0.78; AUC for syphilis=0.84; AUC for gonorrhea=0.78; AUC for chlamydia=0.70) and had stable performance on both external validation data from 2019 (AUC for HIV=0.79; AUC for syphilis=0.85; AUC for gonorrhea=0.81; AUC for chlamydia=0.69) and data from 2020-2021 (AUC for HIV=0.71; AUC for syphilis=0.84; AUC for gonorrhea=0.79; AUC for chlamydia=0.69). Conclusions: Our web-based risk prediction tool could accurately predict the risk of HIV and STIs for clinic attendees using simple self-reported questions. MySTIRisk could serve as an HIV and STI screening tool on clinic websites or digital health platforms to encourage individuals at risk of HIV or an STI to be tested or start HIV pre-exposure prophylaxis. The public can use this tool to assess their risk and then decide if they would attend a clinic for testing. Clinicians or public health workers can use this tool to identify high-risk individuals for further interventions. SN - 1438-8871 UR - //www.mybigtv.com/2022/8/e37850 UR - https://doi.org/10.2196/37850 UR - http://www.ncbi.nlm.nih.gov/pubmed/36006685 DO - 10.2196/37850 ID - info:doi/10.2196/37850 ER -