基于人工智能算法的脑卒中溶栓药物精准治疗:真实世界研究  

Precise Thrombolytic Treatment for Stroke Using AI-based Algorithms:a Real-world Study

作　　者：沈惠文 林永忠[2] 陈淑良[3] 张立红[4] 马春野[2] 马得原 张策[1] SHEN Huiwen;LIN Yongzhong;CHEN Shuliang;ZHANG Lihong;MA Chunye;MA Deyuan;ZHANG Ce(Development Planning and Quality Management Department,the Second Affiliated Hospital of Dalian Medical University,Dalian 116027,China;Department of Neurology,the Second Affiliated Hospital of Dalian Medical University,Dalian 116027,China;Nursing Department,the Second Affiliated Hospital of Dalian Medical University,Dalian 116027,China;Department of Intervention and Neurocritical Care,Dalian Municipal Central Hospital,Dalian 116027,China)

机构地区：[1]大连医科大学附属第二医院发展规划与质量管理部,辽宁省大连市116027 [2]大连医科大学附属第二医院神经内科,辽宁省大连市116027 [3]大连医科大学附属第二医院护理部,辽宁省大连市116027 [4]辽宁省大连市中心医院介入及神经重症科,116027

出　　处：《中国全科医学》2023年第17期2070-2077,2088,共9页Chinese General Practice

基　　金：大连医科大学附属第二医院“1+X”计划临床研究孵化项目(2022LCYJYB05)。

摘　　要：背景缺血性脑卒中(IS)起病急,治疗时间窗窄,治疗效果的影响因素复杂,患者自身情况各异,因此治疗方式、给药种类、给药剂量、给药方式均会影响患者的溶栓效果。既往研究常利用统计方法分析溶栓效果的影响因素,人工智能算法在该方面的临床应用尚少见。目的 基于真实世界的数据,建立IS患者从一般特征、药物治疗方式到恢复效果的人工智能算法模型,实现个体化溶栓药物精准治疗,为临床用药决策提供数据支持。方法 采用回顾性研究方式,从大连医科大学附属第二医院医渡云科研大数据服务器系统提取本院确诊为IS患者(n=55 621)的临床信息,时间为2001-01-01至2021-12-31。依据纳入标准共筛选出信息完整的IS患者1 855例,依据每位患者入院与出院时美国国立卫生研究院卒中量表(NIHSS)评分差值评价患者溶栓效果,并将患者分为神经功能改善组(差值≥4分,n=1 236)和对照组(差值<4分,n=619)。经3位神经内科高级职称专家背对背推荐,并结合查阅的IS诊治指南及文献,整理可能与IS发作后溶栓效果相关的影响因素,归类为患者一般特征、用药指标、检查指标、检验指标、治疗方式5类。首先进行影响因素的单因素筛选,再利用主成分分析法对影响因素做降维处理。构建Logistic回归模型、支持向量机(SVM)、C5.0决策树、分类回归树(CART)、深度神经网络(DNN)及Wide&Deep模型,进行模型对比评价,比较不同模型对IS患者溶栓效果的预测情况,确定最佳模型,进而寻找模型的最优参数。将1 855例患者的临床信息进行分割处理,随机数为7和11,随机分为训练集(1 113例)、验证集(371例)、测试集(371例),其中训练集用来构建和训练模型以发现规律,验证集用来调整模型参数,测试集用来评价最终模型的泛化能力。应用特征工程构建简化模型并评估模型准确度。从大连市中心医院的医渡云科研大数Background The thrombolytic effect for ischemic stroke(IS) is affected by complex factors,such as acute onset of stroke,short therapeutic time window,various individual patient factors,treatment model,types and doses of medicines as well as mode of administration.To identify the influencing factors of thrombolytic effect,most existing studies adopt statistical methods,while rare studies use artificial intelligence(AI)-based algorithms.Objective To establish models using AI-based algorithms for IS patients based on the real-world data including general patient characteristics,medication model and recovery effects,to achieve precise individualized thrombolytic treatment and provide data support for clinical prescription decisions.Methods A retrospective design was used.The clinical information of IS patients(n=55 621) was extracted from the Yidu Cloud scientific research big data server system of the Second Affiliated Hospital of Dalian Medical University from January 1,2001 to December 31,2021,among whom 1 855 with complete information were enrolled according to the inclusion criteria.Thrombolysis effect was evaluated by comparing the National Institutes of Health Stroke Scale(NIHSS) score measured at admission and discharge,and those with an improvement in the NIHSS score by ≥ 4 points and <4 points were assigned to neurological improvement group(n=1 236),and control group(n=619),respectively.Factors possibly associated with post-IS thrombolytic effect(including general patient characteristics,medication indicators,examination indicators,test indicators,and treatment methods) were obtained by summarizing the factors suggested separately by three neurology experts with a senior title,and reviewing relevant guidelines and literature,then were screened using univariate analysis,and the identified ones were treated by dimensionality reduction using principal component analysis(PCA).Models of Logistic,support vector machine(SVM),C5.0 decision tree arithmetic,classification and regression tree(CART),deep neural netwo

关 键 词：缺血性卒中 溶栓药物 人工智能算法 Wide&Deep模型 精准治疗 

分 类 号：R743.3[医药卫生—神经病学与精神病学]