Lino Ferreira Da Silva Barros, Maicon HervertonDa Silva Neto, Sebastião RogerioAlmeida Rodrigues, Maria GabrielaDe Souza Sampaio, VandersonEndo, Patricia Takako2022-12-272022-12-272023-01-03978-0-9981331-6-42b5f4d38-86a4-4489-8632-92a731bb778dhttps://hdl.handle.net/10125/102803Tuberculosis (TB) is a disease with a global impact that over the years has mainly affected the poorest countries. After confirming the TB diagnosis, the health professional needs to analyze the severity of the clinical situation of the patient in order to make decisions about their treatment, which may include admission to Intensive Care Unit (ICU). The aim of this paper is to present a systematic review focused on Machine Learning (ML) models for predicting TB treatment outcomes. From 253 articles found through a boolean search, only 12 of them were classified as relevant, presented and discussed in this work. Results show that the current literature is focused on binary classification, mainly using tree-based ML algorithms. Based on the results of this systematic review, we state that there are many opportunities to develop new scientific projects in this area, highlighting the need for rigorous methodology to conduct models' configuration as well as experiments to evaluate them.10engAttribution-NonCommercial-NoDerivatives 4.0 InternationalService Analyticsartificial intelligencepredictionprognosistreatment outcomestuberculosistext10.24251/HICSS.2023.173