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
Machine 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