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

HIV/AIDS/STI Prevention and Care (1045) Electronic Health Records (1322) Innovative Methods in Public Health and Surveillance (385) Surveillance Systems (514) Clinical Information and Decision Making (1927)

Published on in Vol 9 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/43017, first published .
Predictive Modeling of Lapses in Care for People Living with HIV in Chicago: Algorithm Development and Interpretation

Predictive Modeling of Lapses in Care for People Living with HIV in Chicago: Algorithm Development and Interpretation

Predictive Modeling of Lapses in Care for People Living with HIV in Chicago: Algorithm Development and Interpretation

Authors of this article:

Joseph A Mason1 Author Orcid Image ;   Eleanor E Friedman1 Author Orcid Image ;   Samantha A Devlin1 Author Orcid Image ;   John A Schneider1 Author Orcid Image ;   Jessica P Ridgway1 Author Orcid Image
Article Authors Cited by (3) Tweetations (2) Metrics

Journals

  1. Ridgway J, Mason J, Friedman E, Oliwa T, Flores J, Simon J, Ekong A, Yang T, Schneider J. Comparison of machine learning models to predict loss to follow-up among people with Human Immunodeficiency Virus (HIV). JAMIA Open 2025;8(4) View
  2. 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
  3. Tian J, Tran D, Rustam Z, Zhu G, Nagy P, Kharrazi H, Crews D, Wang Z, Zeger S, Cai C. Identifying Patients at High Risk of Lapses in Diabetic Retinopathy Care: A Machine Learning Study. Ophthalmology Science 2026;6(1):100967 View