Class-imbalance Prediction and High-dimensional Risk Factor Identification of Adverse Reactions of Traditional Chinese Medicine with Centralized Monitoring in Real-world Hospitals
10.13422/j.cnki.syfjx.20230352
- VernacularTitle:真实世界医院集中监测中药不良反应的类不平衡高维预测及其风险因素分类识别
- Author:
Feibiao XIE
1
;
Yehui PENG
1
;
Wei YANG
2
;
Jinfa TANG
3
;
Juan LIU
4
;
Weixia LI
3
;
Hui ZHANG
3
;
Dongyuan WU
5
;
Yali WU
3
;
Yuanming LENG
6
;
Xinghua XIANG
1
Author Information
1. School of Mathematics and Computational Science，Hunan University of Science and Technology，Xiangtan 411201，China
2. Institute of Basic Research in Clinical Medicine， China Academy of Chinese Medical Sciences，Beijing 100700，China
3. Henan Engineering Research Center for Clinical Application，Evaluation and Transformation of Traditional Chinese Medicine，The First Affiliated Hospital of Henan University of Chinese Medicine，Zhengzhou 450000，China
4. China Academy of Inspection and Quarantine，Beijing 100123，China
5. College of Public Health and Health Professions，University of Florida，Florida 32601，USA
6. College of Art and Science，Boston University，Boston 02215，USA
- Publication Type:Journal Article
- Keywords:
hospital centralized monitoring;
adverse drug reaction（ADR）;
cluster resampling;
Danhong injection;
group structure regularization;
class-imbalance high-dimensional prediction;
risk factor identification by category
- From:
Chinese Journal of Experimental Traditional Medical Formulae
2023;29(14):114-122
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveTo achieve high-dimensional prediction of class imbalanced of adverse drug reaction（ADR） of traditional Chinese medicine（TCM） and to classify and identify risk factors affecting the occurrence of ADR based on the post-marketing safety data of TCM monitored centrally in real world hospitals. MethodThe ensemble clustering resampling combined with regularized Group Lasso regression was used to perform high-dimensional balancing of ADR class-imbalanced data， and then to integrate the balanced datasets to achieve ADR prediction and the risk factor identification by category. ResultA practical example study of the proposed method on a monitoring data of TCM injection performed that the accuracy of the ADR prediction， the prediction sensitivity， the prediction specificity and the area under receiver operating characteristic curve（AUC） were all above 0.8 on the test set. Meanwhile， 40 risk factors affecting the occurrence of ADR were screened out from total 600 high-dimensional variables. And the effect of risk factors on the occurrence of ADR was identified by classification weighting. The important risk factors were classified as follows：past history， medication information， name of combined drugs， disease status， number of combined drugs and personal data. ConclusionIn the real world data of rare ADR with a large amount of clinical variables， this paper realized accurate ADR prediction on high-dimensional and class imbalanced condition， and classified and identified the key risk factors and their clinical significance of categories， so as to provide risk early warning for clinical rational drug use and combined drug use， as well as scientific basis for reevaluation of safety of post-marketing TCM.
