2023
Trends in Upper Gastrointestinal Bleeding in Patients on Primary Prevention Aspirin: A Nationwide Emergency Department Sample Analysis, 2016-2020
Li D, Laine L, Shung D. Trends in Upper Gastrointestinal Bleeding in Patients on Primary Prevention Aspirin: A Nationwide Emergency Department Sample Analysis, 2016-2020. The American Journal Of Medicine 2023, 136: 1179-1186.e1. PMID: 37696350, PMCID: PMC10841721, DOI: 10.1016/j.amjmed.2023.08.010.Peer-Reviewed Original ResearchMeSH KeywordsAgedAnti-Inflammatory Agents, Non-SteroidalAspirinCardiovascular DiseasesEmergency Service, HospitalGastrointestinal HemorrhageHumansMedicarePrimary PreventionRisk FactorsUnited StatesConceptsUpper gastrointestinal bleedingGastrointestinal bleedingRed blood cell transfusionNationwide Emergency Department SamplePrimary cardiovascular preventionRecent guideline recommendationsBlood cell transfusionProportion of hospitalizationsEmergency Department SampleMedicare reimbursementInternational Statistical ClassificationRelated Health ProblemsCardiovascular preventionCell transfusionOlder patientsHospital admissionCommon etiologyGuideline recommendationsMajor complicationsUlcer diseaseEndoscopic interventionRevision codesAppropriate indicationsRecent guidelinesCardiovascular disease
2021
Neural network predicts need for red blood cell transfusion for patients with acute gastrointestinal bleeding admitted to the intensive care unit
Shung D, Huang J, Castro E, Tay JK, Simonov M, Laine L, Batra R, Krishnaswamy S. Neural network predicts need for red blood cell transfusion for patients with acute gastrointestinal bleeding admitted to the intensive care unit. Scientific Reports 2021, 11: 8827. PMID: 33893364, PMCID: PMC8065139, DOI: 10.1038/s41598-021-88226-3.Peer-Reviewed Original ResearchConceptsAcute gastrointestinal bleedingRed blood cell transfusionBlood cell transfusionGastrointestinal bleedingHigh-risk patientsCell transfusionRed blood cellsPatient cohortIntensive Care III (MIMIC-III) critical care databaseIntensive care unit staySevere acute gastrointestinal bleedingPacked red blood cellsBlood cellsCommon gastrointestinal causesLaboratory test featuresTime-updated dataIntensive care unitValidation patient cohortCritical care databaseLarge urban hospitalMedical Information MartInternal validation setGastrointestinal causesUnit stayCare unitAdvancing care for acute gastrointestinal bleeding using artificial intelligence
Shung DL. Advancing care for acute gastrointestinal bleeding using artificial intelligence. Journal Of Gastroenterology And Hepatology 2021, 36: 273-278. PMID: 33624892, DOI: 10.1111/jgh.15372.Peer-Reviewed Original ResearchConceptsElectronic health recordsAcute gastrointestinal bleedingIntegration of machineHealth recordsNeural network modelGastrointestinal bleedingRisk prediction toolsNeural network-based analysisArtificial intelligenceMachine learningDecision supportRisk patientsNetwork modelReal timeMachineAlgorithmPrediction toolsClinical risk scoreLower gastrointestinal bleedingLow-risk patientsHigh-risk patientsProspective clinical trialsTriage of patientsClinician risk assessmentDelivery of careEarly identification of patients with acute gastrointestinal bleeding using natural language processing and decision rules
Shung D, Tsay C, Laine L, Chang D, Li F, Thomas P, Partridge C, Simonov M, Hsiao A, Tay JK, Taylor A. Early identification of patients with acute gastrointestinal bleeding using natural language processing and decision rules. Journal Of Gastroenterology And Hepatology 2021, 36: 1590-1597. PMID: 33105045, DOI: 10.1111/jgh.15313.Peer-Reviewed Original ResearchConceptsNatural language processingElectronic health recordsLanguage processingNLP algorithmSystematized NomenclatureReal timeAcute gastrointestinal bleedingBidirectional Encoder RepresentationsDecision rulesEHR-based phenotyping algorithmsGastrointestinal bleedingRisk stratification scoresEncoder RepresentationsData elementsPhenotyping algorithmStratification scoresHealth recordsAlgorithmPhenotyping of patientsEmergency department patientsTime of presentationRisk stratification modelED reviewDeploymentExternal validation
2020
Machine Learning Prognostic Models for Gastrointestinal Bleeding Using Electronic Health Record Data.
Shung D, Laine L. Machine Learning Prognostic Models for Gastrointestinal Bleeding Using Electronic Health Record Data. The American Journal Of Gastroenterology 2020, 115: 1199-1200. PMID: 32530828, PMCID: PMC7415736, DOI: 10.14309/ajg.0000000000000720.Commentaries, Editorials and LettersMeSH KeywordsElectronic Health RecordsGastrointestinal HemorrhageHumansIntensive Care UnitsMachine LearningPrognosisRetrospective StudiesConceptsRisk assessment toolGastrointestinal bleedingIntensive care unit patientsClinical risk assessment toolCare unit patientsElectronic health record dataHealth record dataLevel of careAssessment toolElectronic health recordsAPACHE IVaHospital mortalityHospital courseUnit patientsPrognostic toolClinical practicePrognostic modelHealth recordsRecord dataBleedingExternal validationPatientsLack of generalizabilityMortalityCare
2019
Early Colonoscopy Does Not Improve Outcomes of Patients With Lower Gastrointestinal Bleeding: Systematic Review of Randomized Trials
Tsay C, Shung D, Stemmer Frumento K, Laine L. Early Colonoscopy Does Not Improve Outcomes of Patients With Lower Gastrointestinal Bleeding: Systematic Review of Randomized Trials. Clinical Gastroenterology And Hepatology 2019, 18: 1696-1703.e2. PMID: 31843595, PMCID: PMC7292779, DOI: 10.1016/j.cgh.2019.11.061.Peer-Reviewed Original ResearchMeSH KeywordsAcute DiseaseColonoscopyGastrointestinal HemorrhageHospitalizationHumansRandomized Controlled Trials as TopicConceptsAcute lower gastrointestinal bleedingLower gastrointestinal bleedingRandomized trialsEarly colonoscopyElective colonoscopyGastrointestinal bleedingSecondary outcomesHemostatic interventionEndoscopic interventionDiagnostic yieldSevere acute lower gastrointestinal bleedingSystematic reviewDual independent reviewTiming of colonoscopyOutcomes of patientsHours of presentationRandom-effects modelRecurrent bleedingPrimary outcomeIndex examinationColonoscopy evaluationBias assessmentObservational studyBleedingColonoscopyValidation of a Machine Learning Model That Outperforms Clinical Risk Scoring Systems for Upper Gastrointestinal Bleeding
Shung DL, Au B, Taylor RA, Tay JK, Laursen SB, Stanley AJ, Dalton HR, Ngu J, Schultz M, Laine L. Validation of a Machine Learning Model That Outperforms Clinical Risk Scoring Systems for Upper Gastrointestinal Bleeding. Gastroenterology 2019, 158: 160-167. PMID: 31562847, PMCID: PMC7004228, DOI: 10.1053/j.gastro.2019.09.009.Peer-Reviewed Original ResearchConceptsUpper gastrointestinal bleedingHospital-based interventionsComposite endpointScoring systemRockall scoreGastrointestinal bleedingClinical riskConsecutive unselected patientsLow-risk patientsClinical scoring systemRisk-scoring systemExternal validation cohortCharacteristic curve analysisInternal validation setOutpatient managementUnselected patientsValidation cohortEmergency departmentMedical CenterGreater AUCPatientsAbstractTextCurve analysisEndpointAUCMachine Learning to Predict Outcomes in Patients with Acute Gastrointestinal Bleeding: A Systematic Review
Shung D, Simonov M, Gentry M, Au B, Laine L. Machine Learning to Predict Outcomes in Patients with Acute Gastrointestinal Bleeding: A Systematic Review. Digestive Diseases And Sciences 2019, 64: 2078-2087. PMID: 31055722, DOI: 10.1007/s10620-019-05645-z.Peer-Reviewed Original ResearchConceptsClinical risk scoreUpper gastrointestinal bleedingGastrointestinal bleedingOutcomes of mortalityRisk scoreSystematic reviewOvert gastrointestinal bleedingAcute gastrointestinal bleedingPrognosis Studies toolRisk of biasFull-text studiesCurrent risk assessment toolsRisk assessment toolHospital stayHemostatic interventionRisk stratificationInclusion criteriaPrognostic performanceHigh riskIndependent reviewersConference abstractsLower riskMedian AUCPatientsMortality