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

Artificial Intelligence (2129) Machine Learning (2076) Infectious Diseases (non-STD/STI) (1726) Antimicrobial Stewardship and Antibiotic Resistance (62) Clinical Information and Decision Making (1915) Innovative Methods in Public Health and Surveillance (383) Decision Support for Health Professionals (1539)

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 Tweetations (6) Metrics

Fuzhen Zhang   1, 2, 3 * , PhD ;   Zilong Yang   4 * , MM ;   Xiaonan Geng   2 , MS ;   Yu Dong   1 , MS ;   Shanshan Li   1 , PhD ;   Cong Yao   1 , MBBS ;   Yuanyuan Shang   1 , MBBS ;   Weicong Ren   1 , PhD ;   Ruichao Liu   1 , MD ;   Haobin Kuang   4 , MBBS ;   Liang Li   1, 2 * , MBBS ;   Yu Pang   1 * , PhD

1 Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing, China

2 Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China

3 School of Public Health (Shenzhen), Shenzhen Key Laboratory of Pathogenic Microbes and Biosafety, Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University, Shenzhen, China

4 Department of Tuberculosis, Guangzhou Chest Hospital/ Guangzhou Key Laboratory of Tuberculosis Research/ State Key Laboratory of Respiratory Disease, Guangzhou, China

*these authors contributed equally

Corresponding Author:

  • Yu Pang, PhD
  • Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute
  • No.9 Beiguan Street, Tongzhou District, 100149, Beijing, China
  • Beijing 101149
  • China
  • Phone: 86 10 89509367
  • Fax: 86 10 89509367
  • Email: pangyupound@163.com