住院患者压力性损伤风险智能预警模型的构建  

Developing an intelligent prediction model of pressure injury for inpatients

作　　者：秦春香[1] 程倪妮 盛江明[5] 胡思卿 陆晶 梁伟 黄旺 张秋香[4] QIN Chunxiang;CHENG Nini;SHENG Jiangming;HU Siqing;LU Jing;LIANG Wei;HUANG Wang;ZHANG Qiuxiang(Health Management Center,The Third Xiangya Hospital of Central South University,Changsha,410013,China)

机构地区：[1]中南大学湘雅三医院健康管理中心,长沙市410013 [2]中南大学湘雅三医院产科,长沙市410013 [3]中南大学湘雅三医院心血管内科,长沙市410013 [4]中南大学湘雅三医院护理部,长沙市410013 [5]中南大学湘雅二医院超声科 [6]中南大学湘雅护理学院 [7]湖南工商大学前沿交叉学院

出　　处：《中国护理管理》2023年第9期1317-1321,共5页Chinese Nursing Management

基　　金：国家自然科学基金青年项目(71704191);湖南省重点研发计划(2021SK2024);湖南省自然科学基金(2021JJ30920)。

摘　　要：目的 :构建并验证住院患者压力性损伤风险智能预警模型,为住院患者的压力性损伤风险管理提供依据和参考。方法 :采用便利抽样法,选取长沙市某三级甲等医院压力性损伤病例和同期非压力性损伤病例作为研究对象,通过结构化查询语句进行预警指标的特征提取,并进行一致性检验;应用随机森林Gini指数结合最优特征集选择进行特征筛选;运用机器学习算法构建预警模型并评价模型性能。结果 :预警指标的智能提取与人工提取的Kappa系数为0.639~1.000,随机森林Gini指数下的特征重要性评分为0.005~0.220,最优特征集筛选了身体约束、机械通气、血管活性药物的使用、意识障碍、水肿、高龄等12个预警指标进入模型构建;比较不同机器学习算法构建的模型性能评价指标发现,随机森林算法构建的模型性能更好。结论 :本研究构建了基于电子病历系统的压力性损伤智能预警模型,模型性能好,可在临床推广应用。Objective:To develop and verify an intelligent early warning model of pressure injury risk in inpatients,and provide reference for the risk management of pressure injury in inpatients.Methods:The pressure injury cases and non-pressure injury cases were selected in a tertiary hospital in Changsha city by convenient sampling method.The intelligent feature extraction of early warning indicators was performed by structured query statements,and the consistency test was carried out.Random forest Gini index combined with the good parsimonious prediction model was used to rank the importance of features.The machine learning algorithms were applied to develop prediction model and evaluate the model performance.Results:The Kappa consistency coefficient between intelligent feature extraction and manual extraction of the indicators were 0.639-1.000.The feature importance of random forest Gini index ranged from 0.005 to 0.220.The good parsimonious prediction model with prediction accuracy screened 12 early warning indicators,including physical restraint,mechanical ventilation,use of vasoactive drugs,disturbance of consciousness,edema,and old age and so on.After comparing the model performance of each prediction model,the one established by random forest was the best.Conclusion:This study developed an intelligent prediction model for pressure injury in inpatients based on healthcare information systems with good model performance.This prediction model is suitable for clinical application.

关 键 词：压力性损伤 电子病历 机器学习 智能预警模型 智慧护理 

分 类 号：R47[医药卫生—护理学] R197[医药卫生—临床医学]