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

Machine Learning (1984) Artificial Intelligence (2008) Clinical Information and Decision Making (1823) Computer-Aided Diagnosis (226) Decision Support for Health Professionals (1479) Hypertension Prevention and Treatment (285)

Published on in Vol 9, No 1 (2021): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/19739, first published .
An Application of Machine Learning to Etiological Diagnosis of Secondary Hypertension: Retrospective Study Using Electronic Medical Records

An Application of Machine Learning to Etiological Diagnosis of Secondary Hypertension: Retrospective Study Using Electronic Medical Records

An Application of Machine Learning to Etiological Diagnosis of Secondary Hypertension: Retrospective Study Using Electronic Medical Records

Authors of this article:

Xiaolin Diao1 Author Orcid Image ;   Yanni Huo1 Author Orcid Image ;   Zhanzheng Yan1 Author Orcid Image ;   Haibin Wang1 Author Orcid Image ;   Jing Yuan2 Author Orcid Image ;   Yuxin Wang1 Author Orcid Image ;   Jun Cai3 Author Orcid Image ;   Wei Zhao2 Author Orcid Image
Article Authors Cited by (17) Tweetations (1) Metrics

Journals

  1. Padmanabhan S, Tran T, Dominiczak A. Artificial Intelligence in Hypertension. Circulation Research 2021;128(7):1100 View
  2. Staffini A, Svensson T, Chung U, Svensson A. Heart Rate Modeling and Prediction Using Autoregressive Models and Deep Learning. Sensors 2021;22(1):34 View
  3. Islam S, Talukder A, Awal M, Siddiqui M, Ahamad M, Ahammed B, Rawal L, Alizadehsani R, Abawajy J, Laranjo L, Chow C, Maddison R. Machine Learning Approaches for Predicting Hypertension and Its Associated Factors Using Population-Level Data From Three South Asian Countries. Frontiers in Cardiovascular Medicine 2022;9 View
  4. Zafari H, Kosowan L, Zulkernine F, Signer A. Diagnosing post-traumatic stress disorder using electronic medical record data. Health Informatics Journal 2021;27(4) View
  5. Zanotto B, Beck da Silva Etges A, dal Bosco A, Cortes E, Ruschel R, De Souza A, Andrade C, Viegas F, Canuto S, Luiz W, Ouriques Martins S, Vieira R, Polanczyk C, André Gonçalves M. Stroke Outcome Measurements From Electronic Medical Records: Cross-sectional Study on the Effectiveness of Neural and Nonneural Classifiers. JMIR Medical Informatics 2021;9(11):e29120 View
  6. Nwanosike E, Conway B, Merchant H, Hasan S. Potential applications and performance of machine learning techniques and algorithms in clinical practice: A systematic review. International Journal of Medical Informatics 2022;159:104679 View
  7. Wu L, Huang L, Li M, Xiong Z, Liu D, Liu Y, Liang S, Liang H, Liu Z, Qian X, Ren J, Chen Y. Differential diagnosis of secondary hypertension based on deep learning. Artificial Intelligence in Medicine 2023;141:102554 View
  8. du Toit C, Tran T, Deo N, Aryal S, Lip S, Sykes R, Manandhar I, Sionakidis A, Stevenson L, Pattnaik H, Alsanosi S, Kassi M, Le N, Rostron M, Nichol S, Aman A, Nawaz F, Mehta D, Tummala R, McCallum L, Reddy S, Visweswaran S, Kashyap R, Joe B, Padmanabhan S. Survey and Evaluation of Hypertension Machine Learning Research. Journal of the American Heart Association 2023;12(9) View
  9. Ciccarelli M, Giallauria F, Carrizzo A, Visco V, Silverio A, Cesaro A, Calabrò P, De Luca N, Mancusi C, Masarone D, Pacileo G, Tourkmani N, Vigorito C, Vecchione C. Artificial intelligence in cardiovascular prevention: new ways will open new doors. Journal of Cardiovascular Medicine 2023;24(Supplement 2):e106 View
  10. Mullen N, Curneen J, Donlon P, Prakash P, Bancos I, Gurnell M, Dennedy M. Treating Primary Aldosteronism-Induced Hypertension: Novel Approaches and Future Outlooks. Endocrine Reviews 2024;45(1):125 View
  11. Zhang S, Yang F, Wang L, Si S, Zhang J, Xue F, Lofgren E. Personalized prediction for multiple chronic diseases by developing the multi-task Cox learning model. PLOS Computational Biology 2023;19(9):e1011396 View
  12. Gudigar A, Kadri N, Raghavendra U, Samanth J, Maithri M, Inamdar M, Prabhu M, Hegde A, Salvi M, Yeong C, Barua P, Molinari F, Acharya U. Automatic identification of hypertension and assessment of its secondary effects using artificial intelligence: A systematic review (2013–2023). Computers in Biology and Medicine 2024;172:108207 View
  13. Zeicu C, Fisk M, Evans N. Investigating secondary hypertension in cerebrovascular disease. Practical Neurology 2025;25(2):143 View
  14. Li N, Zhu Q, Dang Y, Zhou Y, Cai X, Heizhati M, Zhang D, Yao X, Luo Q, Hu J, Wang G, Wang Y, Wang M, Hong J. Development and Implementation of a Dynamically Updated Big Data Intelligence Platform Using Electronic Medical Records for Secondary Hypertension. Reviews in Cardiovascular Medicine 2024;25(3) View
  15. Armoundas A, Ahmad F, Attia Z, Doudesis D, Khera R, Kyriakoulis K, Stergiou G, Tang W. Controversy in Hypertension: Pro-Side of the Argument Using Artificial Intelligence for Hypertension Diagnosis and Management. Hypertension 2025;82(6):929 View
  16. Iacoviello M, Santamato V, Pagano A, Marengo A. Interpretable AI-driven multi-objective risk prediction in heart failure patients with thyroid dysfunction. Frontiers in Digital Health 2025;7 View

Conference Proceedings

  1. Areeb M, Rehman A, Sujjada A. ETLTC 2025. An ML Framework for the Early Detection and Prediction of Hypertension: Enhancing Diagnostic Accuracy View