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

Machine Learning (1465) Artificial Intelligence (1371) Clinical Information and Decision Making (1407) Computer-Aided Diagnosis (128) Decision Support for Health Professionals (1186) Hypertension Prevention and Treatment (224)

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 (13) Tweetations (1) Metrics

Journals

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  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
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  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
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  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 2024:pn-2024-004169 View