基于机器学习算法构建老年重症病人静脉血栓栓塞症风险预测模型

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Development of risk prediction model for venous thromboembolism in elderlycriticallyill patient based on machine learning

JIN Jiel， XU Qing²，LU Jie'， ZHAO Jiayue'，ZHANG Qing1，KONG Yang1* ， XU Hongmeil* 1.Binzhou Medical Colege，Shandong 25660o China;2.Binzhou Medical College Yantai Affiliated Hospital *Corresponding AuthorKONG Yang，E-mail： kongyang@ bzmc.edu.cn; XU Hongmei，E-mail： hmx58@163.com

AbstractObjective：Todeveloprisk prediction modelforvenous thromboembolism（VTE）inelderlycriticalyillpatients basedon machineleatotaflillltoeUtee hospitalsinShandongprovincewereselectedasstudysubjectsfromJanuary2O2OtoJune2O23.Andclinicaldatawerecolected.The patients wererandomlydivided intotrainigset36cases）andvalidationset273cases）at7：3ratio.heocurrenceofVTEduringICU hospitalizationwasused as theoutcome variable.Predictionmodels wereconstructed using 4machinelearing，namelyrandomforest， extremegradientbosting，supportvectormachines，andgadientbostigdecision tre.Modelperformancewas evaluatedusingmetrs suchasareaunderthecure（AUC）ofreceiveoperatorcharacteristicandBrierscoreandteoptialmodelwasselected.Inteeability analysisfthebestperformingmodelasconductedusingteSHAPlgorithmResults：Amongthe909elderlyrticallillpatients58 developed VTE， with incidence of 28.4% .Among the 4 models，the random forest achieved the higher AUC（O.8O3），accuracy（0.733）， senitivity（.662），ndspecificiy（0.76），alongiththelowestBrierScore（O.171）.onclusions：TheriskpredictionmodelforE inelderlycriallyillpatientsdevelopedbasedonandomforestdemonstratedstrongpredictiveperformance.Itouldproideeference for optimizing VTE management in elderly critically ill patients.

Keywordsmachine learming; elderly;critical illess;venous thromboembolism; prediction model; random forest

doi：10.12102/j.issn.1009-6493.2025.14.002

静脉血栓栓塞症（venousthromboembolism，VTE）包括深静脉血栓形成（deepvenousthrombosis，DVT）和肺栓塞（pulmonarythromboembolism，PE），是仅次于心肌梗死和脑卒中的第三大常见心血管疾病，具有发病率高、病死率高以及复发率高的特点，是重症监护室（intensivecareunit，ICU）病人常见的并发症之一[1-2]。（剩余11466字）