Machine learning-based in-hospital mortality prediction of HIV/AIDS patients with Talaromyces marneffei infection in Guangxi, China.
Minjuan ShiJianyan LinWudi WeiYaqin QinSirun MengXiaoyu ChenYueqi LiRongfeng ChenZongxiang YuanYingmei QinJiegang HuangBingyu LiangYanyan LiaoLi YeHao LiangZhiman XieJunjun JiangPublished in: PLoS neglected tropical diseases (2022)
The XGBoost machine learning model is a good predictor in the hospitalization outcome of HIV/AIDS patients with T. marneffei infection. The model may have potential application in mortality prediction and high-risk factor identification in the talaromycosis population.