2023
Mo1365 IDENTIFYING PATIENTS WITH ACUTE GASTROINTESTINAL BLEEDING USING NOTE TEXT IN THE ELECTRONIC HEALTH RECORD: A HYBRID NATURAL LANGUAGE PROCESSING AND DEEP LEARNING APPROACH
Dupont J, Zheng N, Laine L, Hsiao A, Thomas P, Partridge C, Shung D. Mo1365 IDENTIFYING PATIENTS WITH ACUTE GASTROINTESTINAL BLEEDING USING NOTE TEXT IN THE ELECTRONIC HEALTH RECORD: A HYBRID NATURAL LANGUAGE PROCESSING AND DEEP LEARNING APPROACH. Gastroenterology 2023, 164: s-838. DOI: 10.1016/s0016-5085(23)02935-9.Peer-Reviewed Original ResearchAcute gastrointestinal bleedingElectronic health recordsGastrointestinal bleedingHealth recordsNote textBleedingPatients
2021
Early 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