Predictive Models to Identify Individuals With HIV at Risk of Unsuppressed Viral Load Using Routine Public Health Data

Machine learning algorithms using mandatory reported public health HIV data can predict which PWH will have future unsuppressed viral load. Future work will assess its clinical utility compared to existing data-to-care initiatives.

Authors

Gordon Honerkamp‐Smith
Susan J. Little (ORCID: https://orcid.org/0000-0002-7645-9737)
Thomas Martin (ORCID: https://orcid.org/0000-0002-5935-2348)
Alan Wells (ORCID: https://orcid.org/0000-0002-1637-8150)
Ravi Goyal (ORCID: https://orcid.org/0000-0002-0358-2435)

Institutions

University of California System (US)
University of California San Diego (US)

Publication Details

Journal: JAIDS Journal of Acquired Immune Deficiency Syndromes
Published: 2025-04-03
DOI: https://doi.org/10.1097/qai.0000000000003670
Citations: 1
Primary Topic: HIV/AIDS Research and Interventions
Type: article
Field-Weighted Citation Impact: 1.40

Controls

|||

ALL TIME

JAN

FEB

MAR

APR

MAY

JUN

JUL

AUG

SEP

OCT

article

Predictive Models to Identify Individuals With HIV at Risk of Unsuppressed Viral Load Using Routine Public Health Data

Gordon Honerkamp‐Smith, Susan J. Little, Thomas Martin, Alan Wells et al.

Apr 3, 2025

1 citations

JAIDS Journal of Acquired Immune Deficiency Syndromes

HIV/AIDS Research and Interventions

1.40

article

Predictive Models to Identify Individuals With HIV at Risk of Unsuppressed Viral Load Using Routine Public Health Data

Gordon Honerkamp‐Smith, Susan J. Little, Thomas Martin, Alan Wells, Ravi Goyal

April 3, 2025article en

1 citations

Abstract

Machine learning algorithms using mandatory reported public health HIV data can predict which PWH will have future unsuppressed viral load. Future work will assess its clinical utility compared to existing data-to-care initiatives.

JAIDS Journal of Acquired Immune Deficiency SyndromesVol. 99(4)

University of California System (US), University of California San Diego (US)

Good health and well-being

Openalex Percentile: Top 22%

HIV/AIDS Research and Interventions

1.40

DOI Open Access PDF OpenAlex

AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.