Work

Electronic Health Record–Based Algorithms May Improve Identification of Cases of Atypical Diabetes, Including in Groups Underrepresented in Biomedical Research

Year: 2026

Type: article

Study design: Observational Study

Source: Diabetes Obesity and Cardiometabolic CARE

DOI: 10.2337/doc26-0082

Authors: Victoria Chen, Cristina I. Fernández Hernández, Megan Griff, Julia L. Douvas, Micah Koss, Melton Fan, Will Marshall, Evelyn Greaux, Wyatt Pfau, Marjan Rezaei, Jeremy Graber, Vatsala Singh, Laura K. Wiley, Sridharan Raghavan, Neda Rasouli, Miriam S. Udler, Sara J. Cromer, RADIANT Study Group

Institutions: Massachusetts General Hospital, University of Puerto Rico System, University of Colorado Denver, University of Colorado Hospital, VA Eastern Colorado Health Care System, Washington University in St. Louis, Broad Institute, Harvard University

Language: English

Topic: Machine Learning in Healthcare

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Good health and well-being, Reduced inequalities

Open Access status: closed

Funder: American Diabetes Association

Award: Hunger genes, food choices, and metabolic responses: implications for obesity and type 2 diabetes

OpenAlex ID: W7204485525

FWCI:

Cites: 33

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