2022
Inclusion of social determinants of health improves sepsis readmission prediction models
Amrollahi F, Shashikumar S, Meier A, Ohno-Machado L, Nemati S, Wardi G. Inclusion of social determinants of health improves sepsis readmission prediction models. Journal Of The American Medical Informatics Association 2022, 29: 1263-1270. PMID: 35511233, PMCID: PMC9196687, DOI: 10.1093/jamia/ocac060.Peer-Reviewed Original ResearchConceptsUnplanned readmissionSepsis patientsReadmission modelsClinical/laboratory featuresSocial determinantsUnplanned hospital readmissionHigh-risk patientsObjective clinical dataLow predictive valueReadmission prediction modelsSepsis readmissionsLaboratory featuresSepsis casesHospital readmissionPredictive factorsClinical dataReadmissionHigh riskPredictive valueSDH factorsMedical carePatientsDemographic featuresLarger studyProgram cohort
2021
Machine Learning-based Prediction Models for Diagnosis and Prognosis in Inflammatory Bowel Diseases: A Systematic Review
Nguyen N, Picetti D, Dulai P, Jairath V, Sandborn W, Ohno-Machado L, Chen P, Singh S. Machine Learning-based Prediction Models for Diagnosis and Prognosis in Inflammatory Bowel Diseases: A Systematic Review. Journal Of Crohn's And Colitis 2021, 16: 398-413. PMID: 34492100, PMCID: PMC8919806, DOI: 10.1093/ecco-jcc/jjab155.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsInflammatory bowel diseaseBowel diseaseClinical dataHigh riskRisk predictionSystematic reviewAcute severe ulcerative colitisLongitudinal disease activitySevere ulcerative colitisAdverse clinical outcomesBias assessment toolRisk of biasAvailable clinical dataMachine learning-based prediction modelsPrediction model RiskDisease activityCohort studyUlcerative colitisClinical outcomesTreatment responseClinical applicabilityLearning-based prediction modelsExternal validationPatientsRisk
2016
iCONCUR: informed consent for clinical data and bio-sample use for research
Kim H, Bell E, Kim J, Sitapati A, Ramsdell J, Farcas C, Friedman D, Feupe S, Ohno-Machado L. iCONCUR: informed consent for clinical data and bio-sample use for research. Journal Of The American Medical Informatics Association 2016, 24: 380-387. PMID: 27589942, PMCID: PMC5391727, DOI: 10.1093/jamia/ocw115.Peer-Reviewed Original ResearchConceptsPatient preferencesClinical dataHuman immunodeficiency virus clinicInternal medicine clinicElectronic health record dataHealth record dataAcademic medical centerElectronic health recordsDe-identified dataOutpatient clinicMedicine clinicFamily historyMedical CenterInformed Consent ToolClinical settingClinicHealth recordsRecord dataInformed consent systemPatientsConsent toolRecipientsConsentE-consentParticipants
2014
Sharing my health data: a survey of data sharing preferences of healthy individuals.
Bell E, Ohno-Machado L, Grando M. Sharing my health data: a survey of data sharing preferences of healthy individuals. AMIA Annual Symposium Proceedings 2014, 2014: 1699-708. PMID: 25954442, PMCID: PMC4419941.Peer-Reviewed Original ResearchBig Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost Patients
Bates D, Saria S, Ohno-Machado L, Shah A, Escobar G. Big Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost Patients. Health Affairs 2014, 33: 1123-1131. PMID: 25006137, DOI: 10.1377/hlthaff.2014.0041.Commentaries, Editorials and LettersConceptsBig dataClinical analyticsPrivacy concernsUse casesElectronic health recordsAnalyticsTypes of dataHealth recordsTypes of insightsNecessary analysisSupport of researchHigh-cost patientsUnprecedented opportunityMonitoring devicesCostHealth careAlgorithmMultiple organ systemsRapid progressInfrastructureUS health care systemHealth care systemSystemAdverse eventsClinical data