Studying patterns and predictors of HIV viral suppression using A Big Data approach: a research protocol.
Jiajia ZhangBankole OlatosiXueying YangSharon WeissmanZhenlong LiJianjun HuXiaoming LiPublished in: BMC infectious diseases (2022)
With both extensive data integration and data analytics, the proposed research will: (1) improve the understanding of the complex inter-related effects of longitudinal trajectories of HIV viral suppressions and HIV treatment history while taking into consideration multilevel factors; and (2) develop empirical public health approaches to achieve ending the HIV epidemic through translating the risk prediction model to a multifactorial decision system that enables the feasibility of AI-assisted clinical decisions.
Keyphrases
- big data
- antiretroviral therapy
- hiv positive
- hiv infected
- hiv testing
- human immunodeficiency virus
- artificial intelligence
- hepatitis c virus
- hiv aids
- public health
- men who have sex with men
- machine learning
- randomized controlled trial
- electronic health record
- south africa
- depressive symptoms
- deep learning
- global health
- smoking cessation