2024
Geriatric 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
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
Prediction 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