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Using machine learning models to plan HIV services: Emerging opportunities in design, implementation and evaluation.

T Dzinamarira1, E Mbunge2, I Chingombe3

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Machine learning offers new ways to improve HIV/AIDS prevention and treatment services. Exploring these AI techniques can help overcome existing barriers and achieve global HIV/AIDS targets.

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Area of Science:

  • Public Health
  • Infectious Diseases
  • Health Informatics

Background:

  • HIV/AIDS is a major global health and economic burden, affecting 36 million people.
  • Despite progress, significant barriers hinder achieving UNAIDS 95-95-95 targets, including detection, treatment access, and service delivery.
  • Vulnerable populations face specific challenges in HIV/AIDS prevention and care.

Purpose of the Study:

  • To explore the potential of machine learning (ML) techniques in enhancing HIV/AIDS service design, prediction, implementation, and evaluation.
  • To identify innovative analytical strategies for improving HIV/AIDS prevention, treatment, and awareness.

Main Methods:

  • A rapid review of literature was conducted from October 24 to November 5, 2022.
  • Searches were performed across major electronic databases (PubMed, Google Scholar, Scopus, etc.) using diverse keywords.
  • Inclusion and exclusion criteria were applied to select relevant publications for the review.

Main Results:

  • The review identified emerging opportunities for applying ML in understanding and improving HIV/AIDS services.
  • ML can potentially address challenges in HIV service design, planning, and evaluation.
  • The study highlights the need for innovative analytic strategies to strengthen HIV/AIDS interventions.

Conclusions:

  • Machine learning presents a promising avenue for advancing HIV/AIDS research and interventions.
  • Integrating ML can lead to more effective HIV/AIDS prevention, treatment, and awareness strategies.
  • Further exploration of ML is crucial for overcoming existing barriers and achieving global HIV/AIDS goals.