2021
Sa503 MACHINE LEARNING OUTPERFORMS TRADITIONAL RISK MODEL IN IDENTIFYING HIGH-NEED, HIGH-COST PATIENTS WITH INFLAMMATORY BOWEL DISEASES IN A NATIONALLY REPRESENTATIVE COHORT
Nguyen N, Patel S, Gabunilas J, Qian A, Cecil A, Ohno-Machado L, Sandborn W, Singh S, Chen P. Sa503 MACHINE LEARNING OUTPERFORMS TRADITIONAL RISK MODEL IN IDENTIFYING HIGH-NEED, HIGH-COST PATIENTS WITH INFLAMMATORY BOWEL DISEASES IN A NATIONALLY REPRESENTATIVE COHORT. Gastroenterology 2021, 160: s-525. PMCID: PMC8108304, DOI: 10.1016/s0016-5085(21)01959-4.Peer-Reviewed Original ResearchNationally Representative CohortHigh-cost patientsTraditional risk modelsRepresentative cohortHigh needRisk model
2020
Burden and Outcomes of Fragmentation of Care in Hospitalized Patients With Inflammatory Bowel Diseases: A Nationally Representative Cohort
Nguyen N, Luo J, Ohno-Machado L, Sandborn W, Singh S. Burden and Outcomes of Fragmentation of Care in Hospitalized Patients With Inflammatory Bowel Diseases: A Nationally Representative Cohort. Inflammatory Bowel Diseases 2020, 27: 1026-1034. PMID: 32944753, PMCID: PMC8205632, DOI: 10.1093/ibd/izaa238.Peer-Reviewed Original ResearchConceptsInflammatory bowel diseaseFragmentation of careHigh riskCohort studyBowel diseaseNationwide Readmissions Database 2013Nationally Representative CohortUS cohort studyRisk of surgeryNumber of patientsRepresentative cohort studyHealth care qualityHospital mortalityHospitalized patientsCohort 2Outpatient careChronic diseasesCohort 1Representative cohortPatientsSingle episodeReadmissionHospitalizationCare qualityCare