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1、EnhancingAdverseDrugEventDetectioninElectronicHealthRecordsUsingMolecularStructureSimilarity:ApplicationtoPancreatitis1,21221SantiagoVilar*,RaveHarpaz,LourdesSantana,EugenioUriarte,CarolFriedman1DepartmentofBiomedicalInformatics,ColumbiaUniversityMedicalCenter,NewYork,NewYork,Un
2、itedStatesofAmerica,2DepartmentofOrganicChemistry,FacultyofPharmacy,UniversityofSantiagodeCompostela,SantiagodeCompostela,SpainAbstractBackground:Adversedrugevents(ADEs)detectionandassessmentisatthecenterofpharmacovigilance.Dataminingofsystems,suchasFDA’sAdverseEventReportingSys
3、tem(AERS)andmorerecently,ElectronicHealthRecords(EHRs),canaidintheautomaticdetectionandanalysisofADEs.Althoughdifferentdataminingapproacheshavebeenshowntobevaluable,itisstillcrucialtoimprovethequalityofthegeneratedsignals.Objective:Toleveragestructuralsimilaritybydevelopingmolec
4、ularfingerprint-basedmodels(MFBMs)tostrengthenADEsignalsgeneratedfromEHRdata.Methods:AreferencestandardofdrugsknowntobecausallyassociatedwiththeadverseeventpancreatitiswasusedtocreateaMFBM.ElectronicHealthRecords(EHRs)fromtheNewYorkPresbyterianHospitalwereminedtogeneratestructur
5、eddata.DisproportionalityAnalysis(DPA)wasappliedtothedata,and278possiblesignalsrelatedtotheADEpancreatitisweredetected.CandidatedrugsassociatedwiththesesignalswerethenassessedusingtheMFBMtofindthemostpromisingcandidatesbasedonstructuralsimilarity.Results:TheuseofMFBMasameanstost
6、rengthenorprioritizesignalsgeneratedfromtheEHRsignificantlyimprovedthedetectionaccuracyofADEsrelatedtopancreatitis.MFBMalsohighlightstheetiologyoftheADEbyidentifyingstructurallysimilardrugs,whichcouldfollowasimilarmechanismofaction.Conclusion:Themethodproposedinthispaperprovides
7、evidenceofbeingapromisingadjuncttoexistingautomatedADEdetectionandanalysisapproaches.Citation:VilarS,HarpazR,SantanaL,UriarteE,FriedmanC(2012)EnhancingAdverseDrugEventDetectioninElectronicHealthRecordsUsingMolecularStructureSimilarity:ApplicationtoPancreatitis.PLoSONE7(7):e41471
8、.doi:10.1371/journal.pone.0041471Editor:AdityaB
