2022 年，在埃塞俄比亚阿姆哈拉地区贡德尔大学综合和专科医院，使用机器学习预测接受抗逆转录病毒治疗的艾滋病毒患者的病毒学失败。

BACKGROUND: Treatment with effective antiretroviral therapy (ART) reduces viral load as well as HIV-related morbidity and mortality in HIV-positive patients. Despite the expanded availability of antiretroviral therapy around the world, virological failure remains a serious problem for HIV-positive patients. Thus, Machine learning predictive algorithms have the potential to improve the quality of care and predict the needs of HIV patients by analyzing huge amounts of data, and enhancing prediction capabilities. This study used different machine learning classification algorithms to predict the features that cause virological failure in HIV-positive patients. METHOD: An institution-based secondary data was used to conduct patients who were on antiretroviral therapy at the University of Gondar Comprehensive and Specialized Hospital from January 2020 to May 2022. Patients' data were extracted from the electronic database using a structured checklist and imported into Python version three software for data pre-processing and analysis. Then, seven supervised classification machine-learning algorithms for model development were trained. The performances of the predictive models were evaluated using accuracy, sensitivity, specificity, precision, f1-score, and AUC. Association rule mining was used to generate the best rule for the association between independent features and the target feature. RESULT: Out of 5264 study participants, 1893 (35.06%) males and 3371 (64.04%) females were included. The random forest classifier (sensitivity = 1.00, precision = 0.987, f1-score = 0.993, AUC = 0.9989) outperformed in predicting virological failure among all selected classifiers. Random forest feature importance and association rules identified the top eight predictors (Male, younger age, longer duration on ART, not taking CPT, not taking TPT, secondary educational status, TDF-3TC-EFV, and low CD4 counts) of virological failure based on the importance ranking, and the CD-4 count was recognized as the most important predictor feature. CONCLUSION: The random forest classifier outperformed in predicting and identifying the relevant predictors of virological failure. The results of this study could be very helpful to health professionals in determining the optimal virological outcome.

背景：有效的抗逆转录病毒疗法（ART）的治疗可降低 HIV 阳性患者的病毒载量以及与 HIV 相关的发病率和死亡率。尽管世界各地抗逆转录病毒疗法的可及性不断扩大，但病毒学失败仍然是 HIV 阳性患者的一个严重问题。因此，机器学习预测算法通过分析大量数据，提高预测能力，有潜力改善护理质量并预测 HIV 患者的需求。本研究使用不同的机器学习分类算法来预测导致 HIV 阳性患者病毒学失败的特征。

方法：本研究使用基于机构的二次数据，对 2020 年 1 月至 2022 年 5 月在贡德尔大学综合和专科医院接受抗逆转录病毒治疗的患者进行了研究。使用结构化检查表从电子数据库中提取患者数据，并将其导入 Python 版本 3 软件进行数据预处理和分析。然后，训练了七种有监督分类机器学习算法用于模型开发。使用准确性、敏感性、特异性、精度、f1 分数和 AUC 来评估预测模型的性能。使用关联规则挖掘生成独立特征与目标特征之间的最佳关联规则。

结果：在 5264 名研究参与者中，包括 1893 名（35.06%）男性和 3371 名（64.04%）女性。随机森林分类器（敏感性=1.00、精度=0.987、f1 分数=0.993、AUC=0.9989）在所有选定的分类器中表现最佳，可预测病毒学失败。随机森林特征重要性和关联规则根据重要性排名确定了病毒学失败的前八个预测因子（男性、年龄较小、ART 持续时间较长、未服用 CPT、未服用 TPT、中等教育程度、TDF-3TC-EFV 和低 CD4 计数），CD4 计数被认为是最重要的预测因子特征。

结论：随机森林分类器在预测和识别病毒学失败的相关预测因子方面表现出色。本研究的结果对于卫生专业人员确定最佳病毒学结果可能非常有帮助。