Construction of stress injury risk prediction model in patients with chronic pain based on machine learning
10.3969/j.issn.1671-8348.2025.02.021
- VernacularTitle:基于机器学习的慢性疼痛患者压力性损伤风险预测模型的构建
- Author:
Weijun YI
1
;
Wenqian LUO
;
Zhouqi ZHANG
;
Yong LIU
;
Bitian FAN
;
Lin ZHANG
Author Information
1. 陆军军医大学第二附属医院疼痛与康复医学科,重庆 400037
- Keywords:
chronic pain patients;
pressure injury;
Braden score scale;
random forest;
prediction model
- From:
Chongqing Medicine
2025;54(2):413-417,424
- CountryChina
- Language:Chinese
-
Abstract:
Objective To construct the predictive model of pressure injury(PI)in the patients with chronic pain based on machine learning,and to analyze its accuracy and rationality,so as to provide an evidence for the predictive evaluation of clinical PI.Methods The clinical medical records data of 396 patients with chronic pain and high risk Braden scores hospitalized in a class 3A hospital of Chongqing City from March 2023 to June 2024 were retrospectively analyzed.Based on the Python3.10 programming language,the decision tree model,random forest model,linear regression model,naive Bayes model and K-Means model were con-structed,and the model performances were compared by accuracy,sensitivity,precision,F1 score and area un-der the receiver operating characteristic(ROC)curve(AUC).Results PI occurred in 35 cases with an inci-dence rate of 8.84%.Age,NRS score,pain site and pain affected sleep were the independent influencing fac-tors for the PI occurrence in the patients with chronic pain.Among 5 kinds of PI risk predictive model,the ac-curacy(0.873),sensitivity(0.874),precision(0.848),F1 score(0.844)and ROC AUC(0.81)of the ran-dom forest model were all higher than those of other models.Conclusion The random forest model has a high predictive performance for PI in the patients with chronic pain,and could be used for the screening and man-agement of high risk groups of PI in the patients with chronic pain.
