This paper is in the following e-collection/theme issue:

Machine Learning (1501) Decision Support for Health Professionals (1210) Artificial Intelligence (1421) Clinical Informatics (1050) Clinical Information and Decision Making (1434)

Published on in Vol 9, No 7 (2021): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29226, first published .
Predicting Antituberculosis Drug–Induced Liver Injury Using an Interpretable Machine Learning Method: Model Development and Validation Study

Predicting Antituberculosis Drug–Induced Liver Injury Using an Interpretable Machine Learning Method: Model Development and Validation Study

Predicting Antituberculosis Drug–Induced Liver Injury Using an Interpretable Machine Learning Method: Model Development and Validation Study

Authors of this article:

Tao Zhong1 Author Orcid Image ;   Zian Zhuang2, 3, 4 Author Orcid Image ;   Xiaoli Dong5, 6 Author Orcid Image ;   Ka Hing Wong5, 6 Author Orcid Image ;   Wing Tak Wong5, 6 Author Orcid Image ;   Jian Wang1 Author Orcid Image ;   Daihai He2, 4 Author Orcid Image ;   Shengyuan Liu1 Author Orcid Image
Article Authors Cited by (9) Tweetations (4) Metrics

Journals

  1. Zhong T, Fan Y, Dong X, Guo X, Wong K, Wong W, He D, Liu S. An Investigation of the Risk Factors Associated With Anti-Tuberculosis Drug-Induced Liver Injury or Abnormal Liver Functioning in 757 Patients With Pulmonary Tuberculosis. Frontiers in Pharmacology 2021;12 View
  2. Chen L, Chen S. Prediction of readmission in patients with acute exacerbation of chronic obstructive pulmonary disease within one year after treatment and discharge. BMC Pulmonary Medicine 2021;21(1) View
  3. Woo S, Alhaqqan D, Gildea D, Patel P, Cundra L, Lewis J. Highlights of the drug-induced liver injury literature for 2021. Expert Review of Gastroenterology & Hepatology 2022;16(8):767 View
  4. Zhuparris A, Maleki G, Koopmans I, Doll R, Voet N, Kraaij W, Cohen A, van Brummelen E, De Maeyer J, Groeneveld G. Smartphone and Wearable Sensors for the Estimation of Facioscapulohumeral Muscular Dystrophy Disease Severity: Cross-sectional Study. JMIR Formative Research 2023;7:e41178 View
  5. Ji S, Lu B, Pan X. A nomogram model to predict the risk of drug-induced liver injury in patients receiving anti-tuberculosis treatment. Frontiers in Pharmacology 2023;14 View
  6. Zhang F, Zhang F, Li L, Pang Y. Clinical Utilization of Artificial Intelligence in Predicting Therapeutic Efficacy in Pulmonary Tuberculosis. Journal of Infection and Public Health 2024 View
  7. Xiao Y, Chen Y, Huang R, Jiang F, Zhou J, Yang T. Interpretable machine learning in predicting drug-induced liver injury among tuberculosis patients: model development and validation study. BMC Medical Research Methodology 2024;24(1) View
  8. Lewis J, Korkmaz S, Rizk C, Copeland M. Diagnosis, prevention and risk-management of drug-induced liver injury due to medications used to treat mycobacterium tuberculosis. Expert Opinion on Drug Safety 2024;23(9):1093 View