This paper is in the following e-collection/theme issue:

Clinical Informatics (1335) Machine Learning (2091) HIV/AIDS/STI Prevention and Care (1046) Clinical Information and Decision Making (1932) Electronic Health Records (1328)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/43277, first published .
No-show Prediction Model Performance Among People With HIV: External Validation Study

No-show Prediction Model Performance Among People With HIV: External Validation Study

No-show Prediction Model Performance Among People With HIV: External Validation Study

Authors of this article:

Joseph A Mason1 Author Orcid Image ;   Eleanor E Friedman1 Author Orcid Image ;   Juan C Rojas2 Author Orcid Image ;   Jessica P Ridgway1 Author Orcid Image
Article Authors Cited by (3) Tweetations (5) Metrics

Journals

  1. Carrasco-Hernández R, Valenzuela-Ponce H, Soto-Nava M, García-Morales C, Matías-Florentino M, Wertheim J, Smith D, Reyes-Terán G, Ávila-Ríos S. Unveiling ecological/evolutionary insights in HIV viral load dynamics: Allowing random slopes to observe correlational changes to CpG-contents and other molecular and clinical predictors. Epidemics 2024;47:100770 View
  2. Kwarah W, Vroom F, Dwomoh D, Bosomprah S. Evaluating predictive performance, validity, and applicability of machine learning models for predicting HIV treatment interruption: a systematic review. BMC Global and Public Health 2025;3(1) View
  3. Mayampurath A, Isakka S, Mason J, Nycklemoe S, Friedman E, Ridgway J. Identification of Clinical Phenotypes Among People with HIV Using Electronic Health Record Data. AIDS and Behavior 2025 View