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

Artificial Intelligence (2252) Machine Learning (2173) Infectious Diseases (non-STD/STI) (1738) Antimicrobial Stewardship and Antibiotic Resistance (62) Clinical Information and Decision Making (2007) Innovative Methods in Public Health and Surveillance (397) Decision Support for Health Professionals (1594)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/69998, first published .
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

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 (1) Tweetations (6) 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 View