2024
Human-Algorithmic Interaction Using a Large Language Model-Augmented Artificial Intelligence Clinical Decision Support System
Rajashekar N, Shin Y, Pu Y, Chung S, You K, Giuffre M, Chan C, Saarinen T, Hsiao A, Sekhon J, Wong A, Evans L, Kizilcec R, Laine L, Mccall T, Shung D. Human-Algorithmic Interaction Using a Large Language Model-Augmented Artificial Intelligence Clinical Decision Support System. 2024, 1-20. DOI: 10.1145/3613904.3642024.Peer-Reviewed Original ResearchClinical decision support systemsHuman-computer interactionDecision support systemArtificial intelligenceAI-CDSSIntelligent clinical decision support systemSupport systemIntegration of artificial intelligenceHuman-algorithm interactionsEase-of-useLanguage modelHuman algorithmAI systemsSocio-technological challengesHealth-care providersMedical student participationQualitative themesClinical simulationClinical expertiseUpper gastrointestinal bleedingUsabilityBorderline decisionsLanguageClinical intuitionTrustReview article: Upper gastrointestinal bleeding – review of current evidence and implications for management
Shung D, Laine L. Review article: Upper gastrointestinal bleeding – review of current evidence and implications for management. Alimentary Pharmacology & Therapeutics 2024, 59: 1062-1081. PMID: 38517201, DOI: 10.1111/apt.17949.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsUpper gastrointestinal bleedingManagement of upper gastrointestinal bleedingAcute upper gastrointestinal bleedingPeptic ulcer diseaseOver-the-scope clipPre-endoscopicVariceal bleedingManagement of acute upper gastrointestinal bleedingTreated with prophylactic antibioticsTransjugular intrahepatic portosystemic shuntUpper gastrointestinal bleeding patientsRed blood cell transfusion policyPre-endoscopic managementHigh-risk stigmataSeverity of bleedingEarly enteral feedingHigh-risk patientsMeta-analysisIntrahepatic portosystemic shuntProton pump inhibitorsEvidence-based updateOver-the-scopeHospital-based carePhase of careRandom-effects method
2023
Achieving Value by Risk Stratification With Machine Learning Model or Clinical Risk Score in Acute Upper Gastrointestinal Bleeding: A Cost Minimization Analysis
Shung D, Lin J, Laine L. Achieving Value by Risk Stratification With Machine Learning Model or Clinical Risk Score in Acute Upper Gastrointestinal Bleeding: A Cost Minimization Analysis. The American Journal Of Gastroenterology 2023, 119: 371-373. PMID: 37753930, PMCID: PMC10872988, DOI: 10.14309/ajg.0000000000002520.Peer-Reviewed Original ResearchUpper gastrointestinal bleedingCost-minimization analysisGastrointestinal bleedingUsual careTriage strategiesAcute upper gastrointestinal bleedingClinical risk scoreLow-risk patientsHealthcare payer perspectiveMinimization analysisRisk assessment toolRisk stratificationEmergency departmentPayer perspectiveRisk scoreBleedingAssessment toolCareRisk assessment modelMachine-learning strategiesPatientsCumulative savingsS802 Management of Direct Acting Oral Anticoagulants in Hospitalized Patients With Upper Gastrointestinal Bleeding: A Real-World Observational Study
Kats D, Zheng N, Huebner J, Paracha R, Melo M, DuPont J, Shung D, Li D. S802 Management of Direct Acting Oral Anticoagulants in Hospitalized Patients With Upper Gastrointestinal Bleeding: A Real-World Observational Study. The American Journal Of Gastroenterology 2023, 118: s587-s588. DOI: 10.14309/01.ajg.0000952848.58323.1f.Peer-Reviewed Original ResearchTrends 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 ResearchConceptsUpper 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 diseaseTu1975 PROVIDER TRUST AND PERCEIVED USEFULNESS OF MACHIINE LEARNING RISK STRATIFICATION TOOL FOR ACUTE UPPER GASTROINTESTINAL BLEEDING USING THE TECHNOLOGY ACCEPTANCE MODEL: A PILOT STUDY
Huebner J, Chung S, Kizilcec R, Laine L, Shung D. Tu1975 PROVIDER TRUST AND PERCEIVED USEFULNESS OF MACHIINE LEARNING RISK STRATIFICATION TOOL FOR ACUTE UPPER GASTROINTESTINAL BLEEDING USING THE TECHNOLOGY ACCEPTANCE MODEL: A PILOT STUDY. Gastroenterology 2023, 164: s-1168-s-1169. DOI: 10.1016/s0016-5085(23)03693-4.Peer-Reviewed Original Research
2019
Validation 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