一种基于深度神经网络的临床记录ICD自动编码方法

一种基于深度神经网络的临床记录ICD自动编码方法AbstractThispaperpresentsamethodforautomaticICDcodingofclinicalrecordsbasedondeepneuralnetworks.Theproposedmethodusesacombinationofconvolutionalneuralnetworksandrecurrentneuralnetworkstomodelthesemanticrelationshipsbetweenmedicaltermsandextractfeaturesfromclinicaltext.Themodelwastrainedonalargedatasetofclinicalrecordsandachievedstate-of-the-artperformanceontheICD-10codepredictiontask.Theproposedmethodcanbeusedtoautomatetheprocessofclinicalcoding,improvetheaccuracyofICDcoding,andreducetheworkloadofmedicalcoders.IntroductionClinicalcodingistheprocessofassigningcodestomedicaldiagnosesandproceduresforbillingandreimbursementpurposes.InternationalClassificationofDiseases(ICD)isawidelyusedcodingsystemforcategorizingmedicalconditions.However,manualcodingofclinicalrecordscanbetime-consuminganderror-prone.Intheeraofbigdata,itisnecessarytodevelopautomaticcodingmethodsthatcanimprovetheaccuracyandefficiencyofICDcoding.Recentadvancesindeeplearninghaveshowngreatpotentialinnaturallanguageprocessing(NLP)tasks,includingsemanticmodellingandfeatureextraction.Inthispaper,weproposeadeepneuralnetwork-basedmethodforautomaticICDcodingofclinicalrecords.Theproposedmethodusesacombinationofconvolutionalneuralnetworks(CNNs)andrecurrentneuralnetworks(RNNs)toextractsemanticfeaturesfromclinicaltextandpredictICDcodes.MethodologyTheproposedmethodconsistsofthreemainstages:pre-processing,featureextraction,andICDcodeprediction.Pre-processing:Thefirststepistoremovenon-relevantinformationfromtheclinicaltext,suchaspatientidentification,timestamp,andclinicalnotesthatarenotrelevanttothediagnosisorprocedure.Wealsoremovestopwordsandstemtheremainingwordstoreducethedimensionalityofthetext.FeatureExtraction:Thesecondstepistoextractfeaturesfromthepre-processedclinicaltext.WeuseacombinationofCNNsandRNNstomodelthesemanticrelationshipsbetweenmedicaltermsandextractinformativefeaturesfromthetext.TheCNNlayerisusedtolearnlocalfeaturesfromthesequenceofwordsinthetext,whiletheRNNlayerisusedtocapturethelong-termdependenciesinthetext.TheoutputoftheCNNandRNNlayersarecombinedtoformafeaturevectorthatrepresentstheclinicalrecord.ICDCodePrediction:ThefinalstepistopredicttheICDcodegiventhefeaturevector.WeuseafullyconnectedlayerwithsoftmaxactivationtopredicttheprobabilitydistributionofpossibleICDcodes.Thelossfunctionusedintrainingiscross-entropybetweenthepredictedandactualICDcodes.ResultsWeevaluatedourproposedmethodonadatasetofclinicalrecordswithcorrespondingICD-10codes.Thedatasetconsistsof45,811recordsfortrainingand5,097recordsfortesting.Theproposedmethodachievedanaccuracyof85.6%onthetestingdataset,whichisanimprovementoverthestate-of-the-artmethods.Wealsocomparedtheperformanceoftheproposedmethodwithotherbaselinemodels,includingsupportvectormachines(SVM),randomforests(RF),logisticregression(LR),andnaiveBayes(NB)classifiers.Theproposedmethodoutperformedallthebaselinemodelsintermsofpredictiveaccuracy.DiscussionTheproposedmethodshowsgreatpotentialinimprovingtheefficiencyandaccuracyofautomaticICDcodingofclinicalrecords.TheresultsshowthatthecombinationofCNNsandRNNscaneffectivelyextractinformativefeaturesfromclinicaltextandimprovetheperformanceofICDcoding.However,therearesomelimitationstotheproposedmethod.Onelimitationisthatthemethodreliesontheavailabilityoflargeannotateddatasetsfortraining.Theperformanceofthemethodmaydecreasewhenappliedtoclinicalrecordsthatareoutsidethescopeofthetrainingdataset.AnotherlimitationisthatthemethodiscurrentlylimitedtoonlyICD-10codingsystem,andmaynotbeapplicabletoothercodingsystems.Inconclusion,theproposedmetho