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Ethnicity

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A Novel Use of Artificial Intelligence to Examine Diversity and Hospital Performance.

The Journal of surgical research
BACKGROUND: The US population is becoming more racially and ethnically diverse. Research suggests that cultural diversity within organizations can increase team potency and performance, yet this theory has not been explored in the field of surgery. F...

Prediction of systemic biomarkers from retinal photographs: development and validation of deep-learning algorithms.

The Lancet. Digital health
BACKGROUND: The application of deep learning to retinal photographs has yielded promising results in predicting age, sex, blood pressure, and haematological parameters. However, the broader applicability of retinal photograph-based deep learning for ...

The Need for Ethnoracial Equity in Artificial Intelligence for Diabetes Management: Review and Recommendations.

Journal of medical Internet research
There is clear evidence to suggest that diabetes does not affect all populations equally. Among adults living with diabetes, those from ethnoracial minority communities-foreign-born, immigrant, refugee, and culturally marginalized-are at increased ri...

Using machine learning to estimate the effect of racial segregation on COVID-19 mortality in the United States.

Proceedings of the National Academy of Sciences of the United States of America
This study examines the role that racial residential segregation has played in shaping the spread of COVID-19 in the United States as of September 30, 2020. The analysis focuses on the effects of racial residential segregation on mortality and infect...

A deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes.

Nature communications
Conventional human leukocyte antigen (HLA) imputation methods drop their performance for infrequent alleles, which is one of the factors that reduce the reliability of trans-ethnic major histocompatibility complex (MHC) fine-mapping due to inter-ethn...

Disease ontologies for knowledge graphs.

BMC bioinformatics
BACKGROUND: Data integration to build a biomedical knowledge graph is a challenging task. There are multiple disease ontologies used in data sources and publications, each having its hierarchy. A common task is to map between ontologies, find disease...

Machine learning approaches improve risk stratification for secondary cardiovascular disease prevention in multiethnic patients.

Open heart
OBJECTIVES: Identifying high-risk patients is crucial for effective cardiovascular disease (CVD) prevention. It is not known whether electronic health record (EHR)-based machine-learning (ML) models can improve CVD risk stratification compared with a...