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, 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 entitiesGeriatric End-of-Life Screening Tool Prediction of 6-Month Mortality in Older Patients
Haimovich A, Burke R, Nathanson L, Rubins D, Taylor R, Kross E, Ouchi K, Shapiro N, Schonberg M. Geriatric End-of-Life Screening Tool Prediction of 6-Month Mortality in Older Patients. JAMA Network Open 2024, 7: e2414213. PMID: 38819823, PMCID: PMC11143461, DOI: 10.1001/jamanetworkopen.2024.14213.Peer-Reviewed Original ResearchConceptsElectronic health recordsEmergency departmentObserved mortality rateED encountersEnd-of-Life Screening ToolOlder adultsEnd-of-life preferencesMortality riskIllness criteriaLife-limiting illnessOptimal screening criteriaDays of ED arrivalEHR-based algorithmTertiary care EDLow risk of mortalityHigher mortality riskMortality rateRisk of mortalityHealth recordsReceiver operating characteristic curveIllness diagnosisMain OutcomesED arrivalSerious illnessDemographic subgroupsIdentifying 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 modelIdentifying 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 model
2023
Disparities Associated With Electronic Behavioral Alerts for Safety and Violence Concerns in the Emergency Department
Haimovich A, Taylor R, Chang-Sing E, Brashear T, Cramer L, Lopez K, Wong A. Disparities Associated With Electronic Behavioral Alerts for Safety and Violence Concerns in the Emergency Department. Annals Of Emergency Medicine 2023, 83: 100-107. PMID: 37269262, PMCID: PMC10689576, DOI: 10.1016/j.annemergmed.2023.04.004.Peer-Reviewed Original ResearchConceptsHealth care systemEmergency departmentPatient-level analysisCare systemED visitsLeft-without-being-seenNegative perceptions of patientsElectronic health record dataUnited States health care systemRegional health care systemStates health care systemDiscontinuity of careHealth record dataElectronic health recordsBlack non-Hispanic patientsPerceptions of patientsBlack non-HispanicRetrospective cross-sectional study of adult patientsAdult emergency departmentNon-Hispanic patientsCross-sectional study of adult patientsMixed-effects regression analysisStudy periodRetrospective cross-sectional studyCare deliveryAutomatable end‐of‐life screening for older adults in the emergency department using electronic health records
Haimovich A, Xu W, Wei A, Schonberg M, Hwang U, Taylor R. Automatable end‐of‐life screening for older adults in the emergency department using electronic health records. Journal Of The American Geriatrics Society 2023, 71: 1829-1839. PMID: 36744550, PMCID: PMC10258151, DOI: 10.1111/jgs.18262.Peer-Reviewed Original ResearchConceptsAdvance care planningDecision curve analysisLife screeningComorbidity indexCode statusPrognostic modelHealth systemOlder adultsCurve analysisOlder ED patientsPalliative care interventionsObservational cohort studyEmergency department visitsPalliative care servicesElixhauser Comorbidity IndexReceiver-operating characteristic curveIdentification of patientsMultivariable logistic regressionLarge regional health systemLife-limiting illnessRisk older adultsCode status ordersLife Screening ToolMortality predictive modelsElectronic health records
2022
Neural Natural Language Processing for unstructured data in electronic health records: A review
Li I, Pan J, Goldwasser J, Verma N, Wong W, Nuzumlalı M, Rosand B, Li Y, Zhang M, Chang D, Taylor R, Krumholz H, Radev D. Neural Natural Language Processing for unstructured data in electronic health records: A review. Computer Science Review 2022, 46: 100511. DOI: 10.1016/j.cosrev.2022.100511.Peer-Reviewed Original ResearchNatural language processingElectronic health recordsLanguage processingDeep learning approachHealth recordsRule-based systemNew neural networkVariety of tasksUnstructured dataUnstructured textKnowledge graphEHR applicationsDigital collectionsNeural networkNLP methodsLearning approachWord embeddingsSurvey paperSecondary useMedical dialogueHealthcare eventsTaskProcessingMultilingualityInterpretability
2018
330 Computational Discovery and Visualization of Patient Phenotypes from Emergency Department Electronic Health Records
Haimovich A, Hong W, Taylor R, Mortazavi B. 330 Computational Discovery and Visualization of Patient Phenotypes from Emergency Department Electronic Health Records. Annals Of Emergency Medicine 2018, 72: s130-s131. DOI: 10.1016/j.annemergmed.2018.08.335.Peer-Reviewed Original Research
2016
Impact of point-of-care ultrasonography on ED time to disposition for patients with nontraumatic shock
Hall MK, Taylor RA, Luty S, Allen IE, Moore CL. Impact of point-of-care ultrasonography on ED time to disposition for patients with nontraumatic shock. The American Journal Of Emergency Medicine 2016, 34: 1022-1030. PMID: 26988105, DOI: 10.1016/j.ajem.2016.02.059.Peer-Reviewed Original ResearchConceptsPOC ultrasonographyEmergency departmentNontraumatic shockCare ultrasonographyPropensity scorePropensity score matchElectronic health recordsHospital mortalityShock patientsPrompt diagnosisED arrivalED patientsED physiciansPoint of careRetrospective studyUnique patientsImpact of pointMean reductionPropensity score modelPatientsUltrasonographyED timeDiagnostic ultrasonographyCovariates of timeEvidence of reductionPrediction of In‐hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data–Driven, Machine Learning Approach
Taylor RA, Pare JR, Venkatesh AK, Mowafi H, Melnick ER, Fleischman W, Hall MK. Prediction of In‐hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data–Driven, Machine Learning Approach. Academic Emergency Medicine 2016, 23: 269-278. PMID: 26679719, PMCID: PMC5884101, DOI: 10.1111/acem.12876.Peer-Reviewed Original ResearchConceptsMachine learning approachesElectronic health recordsLearning approachPredictive analyticsMachine learning techniquesRandom forest modelClinical decision support systemBig Data DrivenDecision support systemForest modelLearning techniquesUse casesData-DrivenFacilitate automationTraditional analytic techniquesAnalyticsSupport systemSimple heuristicsNew analyticsHealth recordsSmall setTree modelQuestion of generalizabilityPrediction modelDecision rules