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

Tao Zhong   1 * , BSc ;   Zian Zhuang   2, 3, 4 * , BSc ;   Xiaoli Dong   5, 6 , PhD ;   Ka Hing Wong   5, 6 , PhD ;   Wing Tak Wong   5, 6 , PhD ;   Jian Wang   1 , BSc ;   Daihai He   2, 4 , PhD ;   Shengyuan Liu   1 , PhD

1 Department of Tuberculosis Control, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen, China

2 Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong, China

3 Department of Biostatistics, University of California, Los Angeles, CA, United States

4 Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, China

5 Research Institute for Future Food, The Hong Kong Polytechnic University, Hong Kong, China

6 Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hong Kong, China

*these authors contributed equally

Corresponding Author:

  • Shengyuan Liu, PhD
  • Department of Tuberculosis Control
  • Shenzhen Nanshan Center for Chronic Disease Control
  • Hua Ming Road No 7
  • Nanshan District
  • Shenzhen, 518000
  • China
  • Phone: 86 13543301395
  • Email: jfk@sznsmby.com