Development of an HIV Risk Prediction Model Using Electronic Health Record Data from an Academic Health System in the Southern United States.
Charles M BurnsLeland PungDaniel WittMichael GaoMark P SendakSuresh BaluDouglas KrakowerJulia L MarcusNwora Lance OkekeMeredith E ClementPublished in: Clinical infectious diseases : an official publication of the Infectious Diseases Society of America (2022)
Our machine-learning models were able to effectively predict incident HIV diagnoses including among women. This study establishes feasibility of using these models to identify persons most suitable for PrEP in the South.
Keyphrases
- electronic health record
- hiv positive
- men who have sex with men
- hiv testing
- antiretroviral therapy
- hiv infected
- machine learning
- human immunodeficiency virus
- hepatitis c virus
- hiv aids
- clinical decision support
- cardiovascular disease
- big data
- polycystic ovary syndrome
- artificial intelligence
- south africa
- type diabetes
- adipose tissue
- deep learning
- metabolic syndrome
- breast cancer risk