Power of Big Data in Ending HIV

Overview

Journal AIDS

Specialty Infectious Diseases

Date 2021 Apr 19

PMID 33867484

Citations 6

Authors

Bankole Olatosi

Sten H Vermund

Xiaoming Li

Affiliations

Soon will be listed here.

Abstract

The articles in this special issue of AIDS focus on the application of the so-called Big Data science (BDS) as applied to a variety of HIV-applied research questions in the sphere of health services and epidemiology. Recent advances in technology means that a critical mass of HIV-related health data with actionable intelligence is available for optimizing health outcomes, improving and informing surveillance. Data science will play a key but complementary role in supporting current efforts in prevention, diagnosis, treatment, and response needed to end the HIV epidemic. This collection provides a glimpse of the promise inherent in leveraging the digital age and improved methods in Big Data science to reimagine HIV treatment and prevention in a digital age.

Citing Articles

Challenges and Opportunities in Big Data Science to Address Health Inequities and Focus the HIV Response.

Rucinski K, Knight J, Willis K, Wang L, Rao A, Roach M Curr HIV/AIDS Rep. 2024; 21(4):208-219.

PMID: 38916675 PMC: 11283392. DOI: 10.1007/s11904-024-00702-3.

Predicting Treatment Interruption Among People Living With HIV in Nigeria: Machine Learning Approach.

Ogbechie M, Walker C, Lee M, Abba Gana A, Oduola A, Idemudia A JMIR AI. 2024; 2:e44432.

PMID: 38875546 PMC: 11041440. DOI: 10.2196/44432.

Use of machine learning approaches to predict transition of retention in care among people living with HIV in South Carolina: a real-world data study.

Cai R, Yang X, Ma Y, Zhang H, Olatosi B, Weissman S AIDS Care. 2024; 36(12):1745-1753.

PMID: 38833544 PMC: 11560699. DOI: 10.1080/09540121.2024.2361245.

The deubiquitinating enzyme USP44 suppresses hepatocellular carcinoma progression by inhibiting Hedgehog signaling and PDL1 expression.

Chen S, Zhou B, Huang W, Li Q, Yu Y, Kuang X Cell Death Dis. 2023; 14(12):830.

PMID: 38097536 PMC: 10721641. DOI: 10.1038/s41419-023-06358-y.

Machine learning with routine electronic medical record data to identify people at high risk of disengagement from HIV care in Tanzania.

Fahey C, Wei L, Njau P, Shabani S, Kwilasa S, Maokola W PLOS Glob Public Health. 2023; 2(9):e0000720.

PMID: 36962586 PMC: 10021592. DOI: 10.1371/journal.pgph.0000720.

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