Predicting Chronic Pain and Treatment Outcomes Using Machine Learning Models Based on High-dimensional Clinical Data From a Large Retrospective Cohort.

Journal: Clinical therapeutics

Published Date: May 31, 2024

PURPOSE: To identify factors and indicators that affect chronic pain and pain relief, and to develop predictive models using machine learning.

Han Wu

Department of Thoracic Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Zhaoyuan Chen

Development Research Center, Qingdao City Construction Investment (Group) Co., Ltd., Qingdao, Shandong, China.
Jiahui Gu

Research Center for Analytical Sciences, Department of Chemistry, College of Sciences, Northeastern University, Shenyang, Liaoning Province 110819, China.
Yi Jiang

Institute of Genomic Medicine, Wenzhou Medical University, Wenzhou 325035, China.
Shenjia Gao

Department of Anesthesiology, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Key laboratory of Perioperative Stress and Protection, Shanghai, China.
Wankun Chen

Department of Anesthesiology, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Key laboratory of Perioperative Stress and Protection, Shanghai, China. Electronic address: chenwank@163.com.
Changhong Miao

Department of Anesthesiology, Zhongshan hospital, Fudan University, Shanghai, China.

Adult Aged Chronic Pain Decision Trees Female Humans Logistic Models Machine Learning Male Middle Aged Pain Management Retrospective Studies Risk Factors Treatment Outcome

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