Vimig Socrates, MS
PhD Student, Computational Biology and BioinformaticsAbout
Research
Publications
Featured Publications
How Does ChatGPT Perform on the United States Medical Licensing Examination? The Implications of Large Language Models for Medical Education and Knowledge Assessment
Gilson A, Safranek C, Huang T, Socrates V, Chi L, Taylor R, Chartash D. How Does ChatGPT Perform on the United States Medical Licensing Examination? The Implications of Large Language Models for Medical Education and Knowledge Assessment. JMIR Medical Education 2023, 9: e45312. PMID: 36753318, PMCID: PMC9947764, DOI: 10.2196/45312.Peer-Reviewed Original ResearchDesign and Development of Halyos: A Patient-Facing Visual EHR Interface for Longitudinal Risk Awareness
Mataraso S, Socrates V, Lekschas F, Gehlenborg N. Design and Development of Halyos: A Patient-Facing Visual EHR Interface for Longitudinal Risk Awareness. ACI Open 2022, 06: e123-e128. DOI: 10.1055/s-0042-1749191.Peer-Reviewed Original ResearchSubstitutable Medical ApplicationsInteroperable interfaceWeb applicationInteractive visualizationEHR interfaceResource platformEHR systemsHealth dataPatient portalsClinical measurementsVisualizationMedical applicationsFuture valuesApplicationsInterfaceEHRPlatformPortalRisk scorePatientsTechnologyInformationLongitudinal informationProvidersAbstract ObjectiveExtraction of Diagnostic Reasoning Relations for Clinical Knowledge Graphs
Socrates V. Extraction of Diagnostic Reasoning Relations for Clinical Knowledge Graphs. 2022, 413-421. DOI: 10.18653/v1/2022.acl-srw.33.Peer-Reviewed Original Research
2024
HEART: Learning better representation of EHR data with a heterogeneous relation-aware transformer
Huang T, Rizvi S, Thakur R, Socrates V, Gupta M, van Dijk D, Taylor R, Ying R. HEART: Learning better representation of EHR data with a heterogeneous relation-aware transformer. Journal Of Biomedical Informatics 2024, 159: 104741. PMID: 39476994, DOI: 10.1016/j.jbi.2024.104741.Peer-Reviewed Original ResearchElectronic health recordsElectronic health record datasetDownstream tasksLanguage modelModeling electronic health recordsLearning better representationsPretrained language modelsEntity predictionRepresentation learningAnomaly detectionAttention weightsRelation embeddingsHealthcare applicationsEncoding schemeMed-BERTHigher-order representationsInput sequenceComputational costReadmission predictionPairwise relationshipsEHR dataElectronic health record dataSuperior performanceHeterogeneous contextsMedical entitiesPatient-Representing Population's Perceptions of GPT-Generated Versus Standard Emergency Department Discharge Instructions: Randomized Blind Survey Assessment
Huang T, Safranek C, Socrates V, Chartash D, Wright D, Dilip M, Sangal R, Taylor R. Patient-Representing Population's Perceptions of GPT-Generated Versus Standard Emergency Department Discharge Instructions: Randomized Blind Survey Assessment. Journal Of Medical Internet Research 2024, 26: e60336. PMID: 39094112, PMCID: PMC11329854, DOI: 10.2196/60336.Peer-Reviewed Original ResearchIdentifying signs and symptoms of urinary tract infection from emergency department clinical notes using large language models
Iscoe M, Socrates V, Gilson A, Chi L, Li H, Huang T, Kearns T, Perkins R, Khandjian L, Taylor R. Identifying signs and symptoms of urinary tract infection from emergency department clinical notes using large language models. Academic Emergency Medicine 2024, 31: 599-610. PMID: 38567658, DOI: 10.1111/acem.14883.Peer-Reviewed Original ResearchElectronic health recordsNatural language processingNatural language processing modelsEmergency departmentTransformer-based modelsClinical notesF1-measureClinical decision supportLanguage modelSpaCy modelsU.S. health systemElements of natural language processingPublic health surveillanceConvolutional neural network-based modelProcessing long documentsIdentification of symptomsHealth recordsHealth systemClinician notesNeural network-based modelMedical careHealth surveillanceSymptom identificationEntity recognitionNetwork-based modelAutomated HEART score determination via ChatGPT: Honing a framework for iterative prompt development
Safranek C, Huang T, Wright D, Wright C, Socrates V, Sangal R, Iscoe M, Chartash D, Taylor R. Automated HEART score determination via ChatGPT: Honing a framework for iterative prompt development. Journal Of The American College Of Emergency Physicians Open 2024, 5: e13133. PMID: 38481520, PMCID: PMC10936537, DOI: 10.1002/emp2.13133.Peer-Reviewed Original ResearchPrompt designsChest pain evaluationRule-based logicScore determinationLanguage modelPrivacy safeguardsPrompt improvementExtract insightsPain evaluationClinical notesRate of responseDiagnostic performancePhysician assessmentPrompt testingDetermination of heartChatGPTDesign frameworkNote analysisHeartSubscoresSimulated patientsClinical spaceIdentifying incarceration status in the electronic health record using large language models in emergency department settings
Huang T, Socrates V, Gilson A, Safranek C, Chi L, Wang E, Puglisi L, Brandt C, Taylor R, Wang K. Identifying incarceration status in the electronic health record using large language models in emergency department settings. Journal Of Clinical And Translational Science 2024, 8: e53. PMID: 38544748, PMCID: PMC10966832, DOI: 10.1017/cts.2024.496.Peer-Reviewed Original ResearchElectronic health recordsNatural language processingHealth recordsIncarceration statusSignificant social determinant of healthSocial determinants of healthClinic electronic health recordsEHR databasePopulation health initiativesDeterminants of healthMitigate health disparitiesRacial health inequitiesEmergency department settingICD-10 codesHealth inequalitiesNatural language processing modelsHealth disparitiesHealth initiativesDepartment settingEmergency departmentSystem interventionsICD-10Clinical notesStudy populationLanguage modelCorrection: How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge Assessment
Gilson A, Safranek C, Huang T, Socrates V, Chi L, Taylor R, Chartash D. Correction: How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge Assessment. JMIR Medical Education 2024, 10: e57594. PMID: 38412478, PMCID: PMC10933712, DOI: 10.2196/57594.Peer-Reviewed Original Research
2021
Characterizing physician EHR use with vendor derived data: a feasibility study and cross-sectional analysis
Melnick ER, Ong SY, Fong A, Socrates V, Ratwani RM, Nath B, Simonov M, Salgia A, Williams B, Marchalik D, Goldstein R, Sinsky CA. Characterizing physician EHR use with vendor derived data: a feasibility study and cross-sectional analysis. Journal Of The American Medical Informatics Association 2021, 28: 1383-1392. PMID: 33822970, PMCID: PMC8279798, DOI: 10.1093/jamia/ocab011.Peer-Reviewed Original ResearchConceptsElectronic health recordsEHR timeCross-sectional analysisAmbulatory physiciansPatient timeHealth systemClinical hoursHours of patientsMedStar Health systemYale-New HavenObstetrics/gynecologyNeurology/psychiatryMultivariable analysisPhysician genderCertain medical specialtiesPhysical medicineFemale physiciansEHR usePhysiciansHealth recordsHealthcare systemMedical specialtiesHoursSpecialtiesGender