Work

Validation of a Machine Learning Model That Outperforms Clinical Risk Scoring Systems for Upper Gastrointestinal Bleeding

Year: 2019

Type: article

Study design: Observational Study

Source: Gastroenterology

DOI: 10.1053/j.gastro.2019.09.009

Authors: Dennis L. Shung, Benjamin Au, Richard Andrew Taylor, J. Kenneth Tay, Stig B. Laursen, Adrian J. Stanley, Harry R. Dalton, Jeffrey Ngu, Michael Schultz, Loren Laine

Institutions: Yale University, Stanford University, Odense University Hospital, Glasgow Royal Infirmary, Royal Cornwall Hospital, Christchurch Hospital, Dunedin Public Hospital, VA Connecticut Healthcare System

Language: English

Topic: Gastrointestinal Bleeding Diagnosis and Treatment

Subfield: Gastroenterology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: bronze

Funders: National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, National Center for Advancing Translational Sciences

Awards: Training Program in Investigative Gastroenterology, Yale Clinical and Translational Science Award

OpenAlex ID: W2976369463

FWCI: 17.69

Cites: 53

Cited by: 227

Related to: 10