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

Decision Support for Health Professionals (1694) Electronic Health Records (1422) Machine Learning (2308) Hypertension Prevention and Treatment (318) Cardiac Risk and Cardiac Risk Calculators (123) Cardiac Diagnostics (37) Surveillance Methods in Population and Public Health (128)

Published on in Vol 12 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/58732, first published .
Doctor checks patient's blood pressure with a sphygmomanometer during a medical exam.

Predictive Models for Sustained, Uncontrolled Hypertension and Hypertensive Crisis Based on Electronic Health Record Data: Algorithm Development and Validation

Predictive Models for Sustained, Uncontrolled Hypertension and Hypertensive Crisis Based on Electronic Health Record Data: Algorithm Development and Validation

Authors of this article:

Hieu Minh Nguyen1 Author Orcid Image ;   William Anderson2 Author Orcid Image ;   Shih-Hsiung Chou3 Author Orcid Image ;   Andrew McWilliams4, 5 Author Orcid Image ;   Jing Zhao6 Author Orcid Image ;   Nicholas Pajewski1, 7 Author Orcid Image ;   Yhenneko Taylor1, 8 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Desroche L, Trimaille A, Garban T, Bouleti C. Artificial intelligence in cardiovascular medicine: An exoskeleton for perception, reasoning and action. Archives of Cardiovascular Diseases 2025;118(11):613 View
  2. Joo H, Seo Y, Hong S, Yu C, Shin S, Kim E. Visit-to-visit systolic blood pressure variability and risk of severe hypertensive episodes in a multicenter cohort. American Journal of Hypertension 2026 View