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
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