{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T17:37:35Z","timestamp":1791999455443,"version":"4.4.0"},"reference-count":27,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T00:00:00Z","timestamp":1678665600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006578","name":"Chi Mei Medical Center","doi-asserted-by":"publisher","award":["CMFHR111126"],"award-info":[{"award-number":["CMFHR111126"]}],"id":[{"id":"10.13039\/501100006578","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Chi Mei Medical Center, Chiali","award":["CMFHR111126"],"award-info":[{"award-number":["CMFHR111126"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Tuberculosis (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. Materials and Methods: Adult patients (age \u2265 20 years) who had a TB diagnosis and received treatment from January 2004 to December 2021 were enrolled in the present study. Thirty-six feature variables were used to develop the predictive models with AI. The data were randomly stratified into a training dataset for model building (70%) and a testing dataset for model validation (30%). These algorithms included XGBoost, random forest, MLP, light GBM, logistic regression, and SVM. Results: A total of 2248 TB patients in Chi Mei Medical Center were included in the study; 71.7% were males, and the other 28.3% were females. The mean age was 67.7 \u00b1 16.4 years. The results showed that our models using the six AI algorithms all had a high area under the receiver operating characteristic curve (AUC) in predicting acute hepatitis, respiratory failure, and mortality, and the AUCs ranged from 0.920 to 0.766, 0.884 to 0.797, and 0.834 to 0.737, respectively. Conclusions: Our AI models were good predictors and can provide clinicians with a valuable tool to detect the adverse prognosis in TB patients early.<\/jats:p>","DOI":"10.3390\/diagnostics13061075","type":"journal-article","created":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T02:31:55Z","timestamp":1678674715000},"page":"1075","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Using an Artificial Intelligence Approach to Predict the Adverse Effects and Prognosis of Tuberculosis"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4364-8248","authenticated-orcid":false,"given":"Kuang-Ming","family":"Liao","sequence":"first","affiliation":[{"name":"Department of Internal Medicine, Chi Mei Medical Center, Chiali, Tainan 722013, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chung-Feng","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Medical Research, Chi Mei Medical Center, Tainan 710402, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chia-Jung","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Information Systems, Chi Mei Medical Center, Tainan 710402, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia-Yih","family":"Feng","sequence":"additional","affiliation":[{"name":"Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112201, Taiwan"},{"name":"School of Medicine, National Yang-Ming University, Taipei 112304, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chin-Chung","family":"Shu","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, National Taiwan University Hospital, Taipei 100225, Taiwan"},{"name":"College of Medicine, National Taiwan University, Taipei 100233, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Shan","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Medical Research, Chi Mei Medical Center, Tainan 710402, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,13]]},"reference":[{"key":"ref_1","unstructured":"(2022). Global Tuberculosis Report 2022, World Health Organization. Available online: https:\/\/www.who.int\/teams\/global-tuberculosis-programme\/tb-reports\/global-tuberculosis-report-2022."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"483","DOI":"10.5588\/ijtld.22.0188","article-title":"Clinical standards for the dosing and management of TB drugs","volume":"26","author":"Alffenaar","year":"2022","journal-title":"Int. J. Tuberc. Lung Dis."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.jceh.2012.12.001","article-title":"Hepatotoxicity Related to Anti-tuberculosis Drugs: Mechanisms and Management","volume":"3","author":"Ramappa","year":"2013","journal-title":"J. Clin. Exp. Hepatol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"100236","DOI":"10.1016\/j.jctube.2021.100236","article-title":"Prasenohadi In-hospital mortality of pulmonary tuberculosis with acute respiratory failure and related clinical risk factors","volume":"23","author":"Elhidsi","year":"2021","journal-title":"J. Clin. Tuberc. Other Mycobact. Dis."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1625","DOI":"10.1183\/09031936.00070907","article-title":"Pulmonary tuberculosis with acute respiratory failure","volume":"32","author":"Kim","year":"2008","journal-title":"Eur. Respir. J."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Harris, M., Qi, A., JeaGal, L., Torabi, N., Menzies, D., Korobitsyn, A., Pai, M., Nathavitharana, R.R., and Khan, F.A. (2019). A systematic review of the diagnostic accuracy of artificial intelligence-based computer programs to analyze chest x-rays for pulmonary tuberculosis. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0221339"},{"key":"ref_7","first-page":"00056","article-title":"Tuberculosis control, and the where and why of artificial intelligence","volume":"21","author":"Doshi","year":"2017","journal-title":"ERJ Open Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"101944","DOI":"10.1016\/j.tube.2020.101944","article-title":"A machine learning-based framework for Predicting Treatment Failure in tuberculosis: A case study of six countries","volume":"123","author":"Asad","year":"2020","journal-title":"Tuberculosis"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"900","DOI":"10.1093\/jamia\/ocac003","article-title":"Integrating landmark modeling framework and machine learning algorithms for dynamic prediction of tuberculosis treatment outcomes","volume":"29","author":"Kheirandish","year":"2022","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Sauer, C.M., Sasson, D., Paik, K.E., McCague, N., Celi, L.A., Fern\u00e1ndez, I.S., and Illigens, B.M.W. (2018). Feature selection and prediction of treatment failure in tuberculosis. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0207491"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic Minority Over-sampling Technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"279","DOI":"10.4103\/ijmy.ijmy_130_21","article-title":"Prediction of treatment failure of tuberculosis using support vector machine with genetic algorithm","volume":"10","author":"Kanesamoorthy","year":"2021","journal-title":"Int. J. Mycobacteriol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1080\/17538157.2018.1433676","article-title":"Predicting treatment outcome of drug-susceptible tuberculosis patients using machine-learning models","volume":"44","author":"Hussain","year":"2019","journal-title":"Inform. Health Soc. Care"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Luo, Y., Xue, Y., Liu, W., Song, H., Huang, Y., Tang, G., Wang, F., Wang, Q., Cai, Y., and Sun, Z. (2022). Development of diagnostic algorithm using machine learning for distinguishing between active tuberculosis and latent tuberculosis infection. BMC Infect. Dis., 22.","DOI":"10.1186\/s12879-022-07954-7"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1086047","DOI":"10.3389\/fmolb.2022.1086047","article-title":"Deep learning based CT images automatic analysis model for active\/non-active pulmonary tuberculosis differential diagnosis","volume":"9","author":"Nijiati","year":"2022","journal-title":"Front. Mol. Biosci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2399428","DOI":"10.1155\/2022\/2399428","article-title":"AI-Assisted Tuberculosis Detection and Classification from Chest X-Rays Using a Deep Learning Normalization-Free Network Model","volume":"2022","author":"Acharya","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1136\/thx.51.2.132","article-title":"Risk factors for hepatotoxicity from antituberculosis drugs: A case-control study","volume":"51","author":"Pande","year":"1996","journal-title":"Thorax"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/S0962-8479(96)90098-2","article-title":"Liver injury during antituberculosis treatment: An 11-year study","volume":"77","author":"Wilcke","year":"1996","journal-title":"Tuber. Lung Dis."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1002\/hep.510310129","article-title":"Antituberculosis drug-related liver dysfunction in chronic hepatitis B infection","volume":"31","author":"Wong","year":"2000","journal-title":"Hepatology"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2147\/HMER.S290542","article-title":"Anti-Tuberculosis Drug Induced Hepatotoxicity and Associated Factors among Tuberculosis Patients at Selected Hospitals, Ethiopia","volume":"13","author":"Molla","year":"2021","journal-title":"Hepatic Med. \u00c9vid. Res."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Gezahegn, L.K., Argaw, E., Assefa, B., Geberesilassie, A., and Hagazi, M. (2020). Magnitude, outcome, and associated factors of anti-tuberculosis drug-induced hepatitis among tuberculosis patients in a tertiary hospital in North Ethiopia: A cross-sectional study. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0241346"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1164\/ajrccm\/151.3_Pt_1.867","article-title":"Tuberculosis as a Primary Cause of Respiratory Failure Requiring Mechanical Ventilation","volume":"151","author":"Penner","year":"1995","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1183\/09031936.03.00038703","article-title":"Patient mortality of active pulmonary tuberculosis requiring mechanical ventilation","volume":"22","author":"Lee","year":"2003","journal-title":"Eur. Respir. J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e25721","DOI":"10.1002\/jia2.25721","article-title":"Long-term mortality after tuberculosis treatment among persons living with HIV in Haiti","volume":"24","author":"Joseph","year":"2021","journal-title":"J. Int. AIDS Soc."},{"key":"ref_25","first-page":"1005","article-title":"Survival and Predictors of Death after Successful Treatment among Smear Positive Tuberculosis: A Cohort Study","volume":"5","author":"Moosazadeh","year":"2014","journal-title":"Int. J. Prev. Med."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/j.ijid.2021.07.067","article-title":"Long-term Survival of Treated Tuberculosis Patients in Comparison to a General Population in South India: A Matched Cohort Study","volume":"110","author":"Selvaraju","year":"2021","journal-title":"Int. J. Infect. Dis."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1256","DOI":"10.1183\/09031936.00131107","article-title":"Risk factors for death among tuberculosis cases: Analysis of European surveillance data","volume":"31","author":"Lefebvre","year":"2008","journal-title":"Eur. Respir. J."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/6\/1075\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:53:26Z","timestamp":1760122406000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/6\/1075"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,13]]},"references-count":27,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["diagnostics13061075"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics13061075","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,13]]}}}