View in EDS HTML Full Text PDF Full Text

Applying Machine Learning to Predict Loss to Follow-Up Among People Living With HIV in Haiti Using a National Electronic Medical Record Cohort.

Saved in:
Bibliographic Details
Title: Applying Machine Learning to Predict Loss to Follow-Up Among People Living With HIV in Haiti Using a National Electronic Medical Record Cohort.
Authors: Wu J; Center for Global Health Practice and Impact (CGHPI), Georgetown University Medical Center, Washington, DC, United States., Shaw B; Center for Global Health Practice and Impact (CGHPI), Georgetown University Medical Center, Washington, DC, United States., Oni B; Center for Global Health Practice and Impact (CGHPI), Georgetown University Medical Center, Washington, DC, United States., Jean-Charles K; Solutions S.A., Port-au-Prince, Haiti., Dorestan D; Center for Global Health Practice and Impact (CGHPI), Georgetown University Medical Center, Port-au-Prince, Haiti., Bien-Aime M; Center for Global Health Practice and Impact (CGHPI), Georgetown University Medical Center, Port-au-Prince, Haiti., Compere-Louis D; Center for Global Health Practice and Impact (CGHPI), Georgetown University Medical Center, Washington, DC, United States., Dorce V; Center for Global Health Practice and Impact (CGHPI), Georgetown University Medical Center, Port-au-Prince, Haiti., Jean-Pierre V; Center for Global Health Practice and Impact (CGHPI), Georgetown University Medical Center, Port-au-Prince, Haiti., Joseph ME; Center for Global Health Practice and Impact (CGHPI), Georgetown University Medical Center, Port-au-Prince, Haiti., Labbe NR; Center for Global Health Practice and Impact (CGHPI), Georgetown University Medical Center, Port-au-Prince, Haiti.
Source: International journal of public health [Int J Public Health] 2026 Apr 28; Vol. 71, pp. 1609496. Date of Electronic Publication: 2026 Apr 28 (Print Publication: 2026).
Publication Type: Journal Article
Journal Info: Publisher: Frontiers Media S.A Country of Publication: Switzerland NLM ID: 101304551 Publication Model: eCollection Cited Medium: Internet ISSN: 1661-8564 (Electronic) Linking ISSN: 16618556 NLM ISO Abbreviation: Int J Public Health Subsets: MEDLINE
Database: MEDLINE Ultimate
Full text is not displayed to guests.
Description
ISSN:1661-8564
DOI:10.3389/ijph.2026.1609496