基于BP神经网络构建儿童肺炎支原体混合腺病毒感染的重症肺炎预测模型

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关键词：肺炎，支原体;腺病毒，人;同时感染;模型，统计学;儿童;BP神经网络;沙普利加法解释中图分类号： R446.+ ，R75.63 文献标志码：A DOI：0.958/0593

Abstract：ObjectiveTo construct a clinical prediction model for severe pneumonia caused by mycoplasma pneumoniae （MP）adenovirus （ADV） in children based on thebackpropagation method （BP） neural network.Methods Aretrospective analysis was conducted onthe clinical，aboratory imaging data 38children withsevere pneumonia caused by MP mixed with ADV infection.Theresearch subjects wereromly divided into the training set thetest set （7：3），aBPneuralnetwork predictionmodelwasconstructed.Thecontributionclinicalfeaturesinthetrainingsetwas quantified byshapleyadditive explanations （SHAP）.The predictors severe pneumonia for MP mixed with ADV were screnedout.It isverifiedthrough theaccuracyrate，lossvalueconfusion matrix inthetestset.ResultsInthesevere group，the duration fever， the highest body temperature，neutrophil percentage （N % ），aspartate transaminase （AST），lactate dehydrogenase（LDH），interleukin-6（IL-6）extensiveinflammatoryconsolidationlengthhospitalstaywereighr than those in the non-severe group，while lymphocyte percentage （ % ） albumin levels were lower than those in the nonsevere group （ P<0.05 ）.Further research results through BP neural network showed that the duration fever，AST，N % ， maximum body temperature，large areas inflammatory consolidation，IL-6， % LDH were the key predictors severe pneumonia causedby MPADV infection.In constructing the clinical prediction model severe MP mixed with ADV in children， the test set showed an accuracy rate 90.48% a loss value O.33 . ConclusionThe prediction model for severe pneumonia caused by mixed MP-ADV infections issuccesfullyconstructed in children using a BP neural network. Theeightkeypredictorsareidentifiedfromthemodelthatcanserveasareferenceforearlyclinical identification severe cases.

Keywords：peumonia，mycopasa;denoviuses，man;ection;models，statistical;child;Beuraletwork;sply additive explanations

社区获得性肺炎（community-acquired pneumonia，CAP）是儿童呼吸道常见的感染性疾病[]。（剩余9779字）