A machine learning model for predicting abnormal liver function induced by a Chinese herbal medicine preparation (Zhengqing Fengtongning) in patients with rheumatoid arthritis based on real-world study.
10.1016/j.joim.2024.12.001
- Author:
Ze YU
1
;
Fang KOU
1
;
Ya GAO
2
;
Fei GAO
3
;
Chun-Ming LYU
4
,
5
;
Hai WEI
6
Author Information
1. Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
2. Department of Pharmacy, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 100037, China.
3. Beijing Medicinovo Technology Co. Ltd., Beijing 100071, China.
4. Experiment Center for Science and Technology, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China. Electronic address: chunming83g@
5. com.
6. Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China. Electronic address: wei_hai@hotmail.com.
- Publication Type:Research Support, Non-U.S. Gov't
- Keywords:
Abnormal liver function;
Machine learning;
Medicine, Chinese traditional;
Real world;
Rheumatoid arthritis;
Zhengqing Fengtongning
- MeSH:
Humans;
Arthritis, Rheumatoid/drug therapy*;
Drugs, Chinese Herbal/therapeutic use*;
Female;
Middle Aged;
Male;
Retrospective Studies;
Machine Learning;
Adult;
Aged;
Liver/physiopathology*;
Alanine Transaminase/blood*
- From:
Journal of Integrative Medicine
2025;23(1):25-35
- CountryChina
- Language:English
-
Abstract:
OBJECTIVE:Rheumatoid arthritis (RA) is a systemic autoimmune disease that affects the small joints of the whole body and degrades the patients' quality of life. Zhengqing Fengtongning (ZF) is a traditional Chinese medicine preparation used to treat RA. ZF may cause liver injury. In this study, we aimed to develop a prediction model for abnormal liver function caused by ZF.
METHODS:This retrospective study collected data from multiple centers from January 2018 to April 2023. Abnormal liver function was set as the target variable according to the alanine transaminase (ALT) level. Features were screened through univariate analysis and sequential forward selection for modeling. Ten machine learning and deep learning models were compared to find the model that most effectively predicted liver function from the available data.
RESULTS:This study included 1,913 eligible patients. The LightGBM model exhibited the best performance (accuracy = 0.96) out of the 10 learning models. The predictive metrics of the LightGBM model were as follows: precision = 0.99, recall rate = 0.97, F1_score = 0.98, area under the curve (AUC) = 0.98, sensitivity = 0.97 and specificity = 0.85 for predicting ALT < 40 U/L; precision = 0.60, recall rate = 0.83, F1_score = 0.70, AUC = 0.98, sensitivity = 0.83 and specificity = 0.97 for predicting 40 ≤ ALT < 80 U/L; and precision = 0.83, recall rate = 0.63, F1_score = 0.71, AUC = 0.97, sensitivity = 0.63 and specificity = 1.00 for predicting ALT ≥ 80 U/L. ZF-induced abnormal liver function was found to be associated with high total cholesterol and triglyceride levels, the combination of TNF-α inhibitors, JAK inhibitors, methotrexate + nonsteroidal anti-inflammatory drugs, leflunomide, smoking, older age, and females in middle-age (45-65 years old).
CONCLUSION:This study developed a model for predicting ZF-induced abnormal liver function, which may help improve the safety of integrated administration of ZF and Western medicine. Please cite this article as: Yu Z, Kou F, Gao Y, Lyu CM, Gao F, Wei H. A machine learning model for predicting abnormal liver function induced by a Chinese herbal medicine preparation (Zhengqing Fengtongning) in patients with rheumatoid arthritis based on real-world study. J Integr Med. 2025; 23(1): 25-35.
