Predicting unfavorable tuberculosis outcomes using machine learning: a prospective cohort

Abstract Background Tuberculosis (TB) continues to be a primary cause of mortality from a singular infectious agent worldwide, with a significant number of patients still encountering unfavorable outcomes such as treatment failure, relapse, or death. Early identification of high-risk individuals is...

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Autors principals:	Taehyung Lee, Inseo Choi, Hoyoun Lee, Hyung Woo Kim, Eung Gu Lee, Yeonhee Park, Sung Soo Jung, Jin Woo Kim, Jee Youn Oh, Heayon Lee, Seung Hoon Kim, Sun-Hyung Kim, Jiwon Lyu, Sun Jung Kwon, Yun-Jeong Jeong, Hyeon-Kyoung Koo, Ju Sang Kim, Jinsoo Min
Format:	Artigo
Idioma:	Inglês
Publicat:	BMC 2026-06-01
Col·lecció:	Tropical Medicine and Health
Matèries:	Tuberculosis Machine learning Treatment outcome XGBoost Serum albumin Nutritional status
Accés en línia:	https://doi.org/10.1186/s41182-026-00969-9
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