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

Artificial Intelligence (4877) Clinical Information and Decision Making (3794) Decision Support for Health Professionals (2282) Machine Learning (3253) Infectious Diseases (non-STD/STI) (1942) Innovative Methods in Public Health and Surveillance (516) Antimicrobial Stewardship and Antibiotic Resistance (82)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/69998, first published .
Doctors examine chest x-ray, highlighting potential lung issue

Using Machine Learning Methods to Predict Early Treatment Outcomes for Multidrug-Resistant or Rifampicin-Resistant Tuberculosis to Enhance Patient Cure Rates: Development and Validation of Multiple Models

Using Machine Learning Methods to Predict Early Treatment Outcomes for Multidrug-Resistant or Rifampicin-Resistant Tuberculosis to Enhance Patient Cure Rates: Development and Validation of Multiple Models

Authors of this article:

Fuzhen Zhang1, 2, 3 Author Orcid Image ;   Zilong Yang4 Author Orcid Image ;   Xiaonan Geng2 Author Orcid Image ;   Yu Dong1 Author Orcid Image ;   Shanshan Li1 Author Orcid Image ;   Cong Yao1 Author Orcid Image ;   Yuanyuan Shang1 Author Orcid Image ;   Weicong Ren1 Author Orcid Image ;   Ruichao Liu1 Author Orcid Image ;   Haobin Kuang4 Author Orcid Image ;   Liang Li1, 2 Author Orcid Image ;   Yu Pang1 Author Orcid Image
Article Authors Cited by (8) Tweetations (4) Metrics

Journals

  1. Emegano D, Ozsahin I, Isaac E, Ozsahin D, Silas O, Emegano C, Uzun B. Artificial intelligence and diagnosis and management of tuberculosis disease in children. Current Opinion in Pediatrics 2026;38(2):155 View
  2. Paintsil E. Growing bromance between infectious diseases and artificial intelligence: for better or for worse. Current Opinion in Pediatrics 2026;38(2):133 View
  3. Adeboye A, Georgeleen O, Olusesan A, Colin N. Machine learning prediction of tuberculosis mortality: a comparative analysis of random survival forest and cox regression models. BMC Infectious Diseases 2026;26(1) View
  4. Murtiani F, Sudaryo M, Martha E, Handayani D, Lestari A, Febriyani B, Fatimah F, Marisa A. Global Research Trends in Machine Learning and Scoring Systems for Drug-Resistant Tuberculosis Outcome Prediction: A Bibliometric Analysis. Biomedical & Pharmacology Journal 2026;1(19):139 View
  5. Qu S, Hu S, Zhu J, Ye H. Construction and application of a diagnostic model for tuberculosis in patients with pneumoconiosis. Frontiers in Medicine 2026;13 View
  6. Murtiani F, Sudaryo M, Evi M, Handayani D, Umniyati H, Lestari A, Marisa A, Febriani B, Fatimah F. Global research trends in multidrug-resistant tuberculosis treatment outcomes: A Scopus bibliometric analysis. Journal of Interventional Epidemiology and Public Health 2026;9(3) View
  7. Zhang W, Li T, Liu H, Liu M, Song F, Zhou L. Development and validation of a LASSO-derived nomogram for predicting unfavorable treatment outcomes in drug-resistant pulmonary tuberculosis. BMC Infectious Diseases 2026;26(1) View
  8. Zhang F, Yang Z, Jiang M, Hu X, Li J, Liu K, Wang Y, Hu Y, Dong Y, Qin M, Li Q, Li L, Pang Y. Early sputum bacterial load dynamics predict treatment response in multidrug-resistant/rifampicin-resistant tuberculosis: A multicenter prospective cohort study. Journal of Infection 2026;93(4):106838 View