2024
Validation of an Electronic Health Record-Based Machine Learning Model Compared to Clinical Risk Scores for Gastrointestinal Bleeding
Shung D, Chan C, You K, Nakamura S, Saarinen T, Zheng N, Simonov M, Li D, Tsay C, Kawamura Y, Shen M, Hsiao A, Sekhon J, Laine L. Validation of an Electronic Health Record-Based Machine Learning Model Compared to Clinical Risk Scores for Gastrointestinal Bleeding. Gastroenterology 2024 PMID: 38971198, DOI: 10.1053/j.gastro.2024.06.030.Peer-Reviewed Original ResearchElectronic health recordsGlasgow-Blatchford scoreEmergency departmentVery-low-risk patientsRisk scoreOakland scoreMachine learning modelsStructured data fieldsClinical risk scoreGastrointestinal bleedingAll-cause mortalityHealth recordsLearning modelsManual data entrySecondary analysisRisk stratification scoresAssess proportionRed blood-cell transfusionPrimary outcomeProportion of patientsData entryOvert gastrointestinal bleedingPrimary analysisReceiver-operating-characteristic curveVery-low-riskHuman-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 intuitionTrustMo1066 LARGE LANGUAGE MODEL-BASED SIMULATED PATIENTS WITH UPPER GASTROINTESTINAL BLEEDING FOR MEDICAL EDUCATION – A PILOT STUDY WITH EMPATHGPT
Rajashekar N, Chan C, Laine L, Shung D. Mo1066 LARGE LANGUAGE MODEL-BASED SIMULATED PATIENTS WITH UPPER GASTROINTESTINAL BLEEDING FOR MEDICAL EDUCATION – A PILOT STUDY WITH EMPATHGPT. Gastroenterology 2024, 166: s-933. DOI: 10.1016/s0016-5085(24)02628-3.Peer-Reviewed Original ResearchSu1979 GUTGPT: NOVEL LARGE LANGUAGE MODEL PIPELINE OUTPERFORMS OTHER LARGE LANGUAGE MODELS IN ACCURACY AND SIMILARITY TO INTERNATIONAL EXPERTS FOR GUIDELINE RECOMMENDED MANAGEMENT OF PATIENTS WITH UPPER GASTROINTESTINAL BLEEDING
Giuffrè M, You K, Chung S, Kresevic S, Chan C, Saarinen T, Nakamura S, Laine L, Sung J, Garcia-Tsao G, Gralnek I, Barkun A, Sekhon J, Shung D. Su1979 GUTGPT: NOVEL LARGE LANGUAGE MODEL PIPELINE OUTPERFORMS OTHER LARGE LANGUAGE MODELS IN ACCURACY AND SIMILARITY TO INTERNATIONAL EXPERTS FOR GUIDELINE RECOMMENDED MANAGEMENT OF PATIENTS WITH UPPER GASTROINTESTINAL BLEEDING. Gastroenterology 2024, 166: s-889-s-890. DOI: 10.1016/s0016-5085(24)02528-9.Peer-Reviewed Original ResearchTu1649 SIMULATING THE PATIENT-PRACTITIONER RELATIONSHIP IN PATIENTS WITH IRRITABLE BOWEL SYNDROME WITH LARGE LANGUAGE MODELBASED TOOLS – A PROOF OF CONCEPT
Rajashekar N, Chan C, Deutsch J, Laine L, Shung D. Tu1649 SIMULATING THE PATIENT-PRACTITIONER RELATIONSHIP IN PATIENTS WITH IRRITABLE BOWEL SYNDROME WITH LARGE LANGUAGE MODELBASED TOOLS – A PROOF OF CONCEPT. Gastroenterology 2024, 166: s-1364. DOI: 10.1016/s0016-5085(24)03586-8.Peer-Reviewed Original Research407 IMPACT OF ARTIFICIAL INTELLIGENCE SYSTEMS FOR UPPER GASTROINTESTINAL BLEEDING ON CLINICIAN TRUST AND LEARNING USING LARGE LANGUAGE MODELS: A RANDOMIZED PILOT SIMULATION STUDY
Chung S, Rajashekar N, Pu Y, Shin Y, Giuffrè M, Chan C, You K, Saarinen T, Hsiao A, Sekhon J, Wong A, Evans L, McCall T, Kizilcec R, Laine L, Shung D. 407 IMPACT OF ARTIFICIAL INTELLIGENCE SYSTEMS FOR UPPER GASTROINTESTINAL BLEEDING ON CLINICIAN TRUST AND LEARNING USING LARGE LANGUAGE MODELS: A RANDOMIZED PILOT SIMULATION STUDY. Gastroenterology 2024, 166: s-95-s-96. DOI: 10.1016/s0016-5085(24)00715-7.Peer-Reviewed Original ResearchReview 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 Original ResearchUpper 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 savingsTrends 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 disease
2021
MURAL: An Unsupervised Random Forest-Based Embedding for Electronic Health Record Data
Gerasimiuk M, Shung D, Tong A, Stanley A, Schultz M, Ngu J, Laine L, Wolf G, Krishnaswamy S. MURAL: An Unsupervised Random Forest-Based Embedding for Electronic Health Record Data. 2021, 00: 4694-4704. DOI: 10.1109/bigdata52589.2021.9672045.Peer-Reviewed Original ResearchNeural 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 unitEarly 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 LettersConceptsRisk 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 ResearchConceptsAcute 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