2023
Machine learning to develop a predictive model of pressure injury in persons with spinal cord injury
Luther S, Thomason S, Sabharwal S, Finch D, McCart J, Toyinbo P, Bouayad L, Lapcevic W, Hahm B, Hauser R, Matheny M, Powell-Cope G. Machine learning to develop a predictive model of pressure injury in persons with spinal cord injury. Spinal Cord 2023, 61: 513-520. PMID: 37598263, DOI: 10.1038/s41393-023-00924-z.Peer-Reviewed Original ResearchConceptsPressure injuriesAmerican Spinal Cord Injury Association Impairment ScaleSCI/D CentersSpinal cord injury/diseaseReceiver-operating curve analysisNew pressure injuryModifiable risk factorsElectronic health record dataSCI/DSpinal cord injuryHealth record dataInjury/diseaseTwo-step logistic regressionLogistic regression modelsCohort studyRegression modelsStudy designACord injurySevere gradesRisk factorsImpairment ScaleHigh riskClinical implicationsTotal daysAnnual examPROSER: A Web-Based Peripheral Blood Smear Interpretation Support Tool Utilizing Electronic Health Record Data
Iscoe M, Loza A, Turbiville D, Campbell S, Peaper D, Balbuena-Merle R, Hauser R. PROSER: A Web-Based Peripheral Blood Smear Interpretation Support Tool Utilizing Electronic Health Record Data. American Journal Of Clinical Pathology 2023, 160: 98-105. PMID: 37026746, DOI: 10.1093/ajcp/aqad024.Peer-Reviewed Original ResearchConceptsQuality improvement studyElectronic health recordsLaboratory valuesWeb-based clinical decision support toolClinical decision support toolElectronic health record dataHealth record dataImprovement studyResident trainingBlood smear interpretationClinical outcomesMorphologic findingsAcademic hospitalCorresponding reference rangesMedication informationReference rangeMicroscopy findingsCDS toolsIntervention effectsPathology practiceSmear interpretationHealth recordsRecord dataPathologistsPatients
2022
2214. National Testing Assessment of Select Bacterial STIs During the COVID-19 Pandemic
Fonseca G, Maier M, Keddem S, Borgerding J, Lowy E, McFarland L, Comstock E, Van Epps P, Ohl M, Hauser R, Ross D, Beste L. 2214. National Testing Assessment of Select Bacterial STIs During the COVID-19 Pandemic. Open Forum Infectious Diseases 2022, 9: ofac492.1833. DOI: 10.1093/ofid/ofac492.1833.Peer-Reviewed Original ResearchTesting ratesSyphilis testsCOVID-19 pandemicGC testingVeterans Health Administration healthcare systemCT/GCNumber of ChlamydiaOverall testing rateElectronic health record dataAge 25 yearsHealth record dataSTI testing ratesNear-baseline levelsSelf-reported raceBackground ChlamydiaSTI diagnosisSyphilis infectionBacterial STIsSTI testingHIV statusInfection testingPatient groupRetrospective studySyphilis testingPre-pandemic levelsSexually Transmitted Infection Testing in the National Veterans Health Administration Patient Cohort During the Coronavirus Disease 2019 Pandemic
Beste L, Keddem S, Borgerding J, Lowy E, Gardella C, McFarland L, Comstock E, Fonseca G, Van Epps P, Ohl M, Hauser R, Ross D, Maier M. Sexually Transmitted Infection Testing in the National Veterans Health Administration Patient Cohort During the Coronavirus Disease 2019 Pandemic. Open Forum Infectious Diseases 2022, 9: ofac433. PMID: 36514443, PMCID: PMC9452156, DOI: 10.1093/ofid/ofac433.Peer-Reviewed Original ResearchChlamydia/gonorrheaVeterans Health AdministrationHIV testingInfection testingHawaiian/Pacific IslanderTesting ratesChlamydia/gonorrhea testingHuman immunodeficiency virus (HIV) testingTransmitted Infection TestingElectronic health record dataCoronavirus disease 2019 (COVID-19) pandemicHealth record dataSTI testing ratesPacific IslandersDisease 2019 pandemicSlow recoveryNear-total recoveryGonorrhoea testingPreexposure prophylaxisHIV statusRural veteransRetrospective studyPatient cohortSyphilis testingVHA system
2021
Clinical knowledge extraction via sparse embedding regression (KESER) with multi-center large scale electronic health record data
Hong C, Rush E, Liu M, Zhou D, Sun J, Sonabend A, Castro VM, Schubert P, Panickan VA, Cai T, Costa L, He Z, Link N, Hauser R, Gaziano JM, Murphy SN, Ostrouchov G, Ho YL, Begoli E, Lu J, Cho K, Liao KP, Cai T. Clinical knowledge extraction via sparse embedding regression (KESER) with multi-center large scale electronic health record data. Npj Digital Medicine 2021, 4: 151. PMID: 34707226, PMCID: PMC8551205, DOI: 10.1038/s41746-021-00519-z.Peer-Reviewed Original ResearchCode embeddingsKnowledge extractionFeature selectionTraditional data mining approachesClinical knowledge extractionKnowledge mapData mining approachElectronic health record systemsPatient-level dataHealth record systemsDomain expertsMining approachDisease-drug pairsLarge-scale electronic health record dataEHR dataElectronic health record dataRelevant featuresRecord systemComparable performanceEmbeddingHealth record dataRelevant codesMulti-center studySingle-institution dataKESERA Machine Learning Model to Successfully Predict Future Diagnosis of Chronic Myelogenous Leukemia With Retrospective Electronic Health Records Data
Hauser RG, Esserman D, Beste LA, Ong SY, Colomb DG, Bhargava A, Wadia R, Rose MG. A Machine Learning Model to Successfully Predict Future Diagnosis of Chronic Myelogenous Leukemia With Retrospective Electronic Health Records Data. American Journal Of Clinical Pathology 2021, 156: 1142-1148. PMID: 34184028, DOI: 10.1093/ajcp/aqab086.Peer-Reviewed Original ResearchConceptsBlood cell countChronic myelogenous leukemiaCell countMyelogenous leukemiaRetrospective electronic health record dataDiagnosis of CMLLarge integrated health care systemDifferential blood cell countsIntegrated health care systemUsual medical careTime of diagnosisElectronic health record dataClonal stem cell disorderHealth record dataStem cell disordersHealth care systemDisease courseDiagnostic workupAdult leukemiaCell disordersMedical careDiagnostic testingDiagnostic testsBlood cellsCare system