Park, James YeongjunJames YeongjunParkHsu, Tzu-ChunTzu-ChunHsuHu, Jiun-RueyJiun-RueyHuChen, Chun-YuanChun-YuanChenHsu, Wan-TingWan-TingHsuLee, MatthewMatthewLeeHo, JoshuaJoshuaHoCHIEN-CHANG LEE2022-07-292022-07-2920221438-8871https://scholars.lib.ntu.edu.tw/handle/123456789/616084Although machine learning (ML) algorithms have been applied to point-of-care sepsis prognostication, ML has not been used to predict sepsis mortality in an administrative database. Therefore, we examined the performance of common ML algorithms in predicting sepsis mortality in adult patients with sepsis and compared it with that of the conventional context knowledge-based logistic regression approach.enSuperLearner; machine learning; mortality; sepsis[SDGs]SDG3[SDGs]SDG10journal article10.2196/29982354167852-s2.0-85128119078WOS:000800668600002https://api.elsevier.com/content/abstract/scopus_id/85128119078