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

Involvement of Elderly Populations in Health and Health Services Research (107) Cardiology (287) Advanced Data Analytics in eHealth (384) Cardiac Risk and Cardiac Risk Calculators (188)

Published on in Vol 6 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/51844, first published .
Orange defibrillator AED box mounted on a brick wall, with a Samaritan PAD device visible inside.

Joint Modeling of Social Determinants and Clinical Factors to Define Subphenotypes in Out-of-Hospital Cardiac Arrest Survival: Cluster Analysis

Joint Modeling of Social Determinants and Clinical Factors to Define Subphenotypes in Out-of-Hospital Cardiac Arrest Survival: Cluster Analysis

Authors of this article:

Ethan E Abbott1, 2, 3, 4 Author Orcid Image ;   Wonsuk Oh3, 5, 6 Author Orcid Image ;   Yang Dai6 Author Orcid Image ;   Cole Feuer6 Author Orcid Image ;   Lili Chan3, 6, 7 Author Orcid Image ;   Brendan G Carr1, 4 Author Orcid Image ;   Girish N Nadkarni2, 3, 5, 6, 7 Author Orcid Image
Article Authors Cited by (3) Tweetations (7) Metrics

Journals

  1. Oh W, Veshtaj M, Sakhuja A. Enriching patient populations in ICU trials: reducing heterogeneity through machine learning. Current Opinion in Critical Care 2025;31(4):410 View
  2. Miles J, Brady M, Smith L, Cotterill C, Levey C. Use of artificial intelligence in the out-of-hospital care settings: a scoping review. BMJ Digital Health & AI 2026;2(1):e000035 View
  3. Liu X, Chong K, Lau Y, Drum C, Richards A, Lip G, Yeo K, Ong M, Lim S, Leong B, Chia M, Ng W, Mao D, Gan H, Nausheen , Chan M, Ho H, Tan K, Tan N, Lee S, Kwan Y, Chow C, Chong D, Shum K, Ambhore A, Ting J, Lim P. Early post-ROSC laboratory phenotypes and clinical outcomes after out-of-hospital cardiac arrest: a 10-year national cohort study. Resuscitation 2026:111351 View