Liao, Kuang-MingKuang-MingLiaoLiu, Chung-FengChung-FengLiuChen, Chia-JungChia-JungChenFeng, Jia-YihJia-YihFengCHIN-CHUNG SHUMa, Yu-ShanYu-ShanMa2023-10-202023-10-202023-03-132075-4418https://pubmed.ncbi.nlm.nih.gov/36980382/https://scholars.lib.ntu.edu.tw/handle/123456789/636413Tuberculosis (TB) is one of the leading causes of death worldwide and a major cause of ill health. Without treatment, the mortality rate of TB is approximately 50%; with treatment, most patients with TB can be cured. However, anti-TB drug treatments may result in many adverse effects. Therefore, it is important to detect and predict these adverse effects early. Our study aimed to build models using an artificial intelligence/machine learning approach to predict acute hepatitis, acute respiratory failure, and mortality after TB treatment.enacute hepatitis; artificial intelligence; machine learning; mortality; respiratory failure; tuberculosis[SDGs]SDG3journal article10.3390/diagnostics13061075369803822-s2.0-85151619037https://api.elsevier.com/content/abstract/scopus_id/85151619037