Machine Learning Approaches for Detecting Diabetic Retinopathy from Clinical and Public Health Records.

Journal: AMIA ... Annual Symposium proceedings. AMIA Symposium

PMID: 26958235

Abstract

INTRODUCTION: Annual eye examinations are recommended for diabetic patients in order to detect diabetic retinopathy and other eye conditions that arise from diabetes. Medically underserved urban communities in the US have annual screening rates that are much lower than the national average and could benefit from informatics approaches to identify unscreened patients most at risk of developing retinopathy.

Authors

Omolola Ogunyemi

Center for Biomedical Informatics, Los Angeles, California; Charles Drew University of Medicine and Science, Los Angeles, California; University of California Los Angeles, Los Angeles, California.
Dulcie Kermah

Biostatistics Core, Los Angeles, California; Charles Drew University of Medicine and Science, Los Angeles, California.

Keywords

Diabetic Retinopathy Humans Machine Learning Medically Underserved Area Nutrition Surveys Public Health Informatics Retinal Diseases Safety-net Providers Sensitivity and Specificity Statistics as Topic United States Urban Population

External Resources

View on PubMed PubMed (26958235)

Machine Learning Approaches for Detecting Diabetic Retinopathy from Clinical and Public Health Records.

Abstract

Authors

Keywords

External Resources

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