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

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