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

Machine Learning (1465) Secondary Use of Clinical Data for Research and Surveillance (366) Cardiovascular Disease Prevention (137) Clinical Information and Decision Making (1407) Decision Support for Health Professionals (1185) Clinical Informatics (1022)

Published on in Vol 10, No 2 (2022): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/30483, first published .
Disease Progression of Hypertrophic Cardiomyopathy: Modeling Using Machine Learning

Disease Progression of Hypertrophic Cardiomyopathy: Modeling Using Machine Learning

Disease Progression of Hypertrophic Cardiomyopathy: Modeling Using Machine Learning

Authors of this article:

Matej Pičulin1 Author Orcid Image ;   Tim Smole1 Author Orcid Image ;   Bojan Žunkovič1 Author Orcid Image ;   Enja Kokalj1 Author Orcid Image ;   Marko Robnik-Šikonja1 Author Orcid Image ;   Matjaž Kukar1 Author Orcid Image ;   Dimitrios I Fotiadis2 Author Orcid Image ;   Vasileios C Pezoulas2 Author Orcid Image ;   Nikolaos S Tachos2 Author Orcid Image ;   Fausto Barlocco3, 4 Author Orcid Image ;   Francesco Mazzarotto3, 4, 5 Author Orcid Image ;   Dejana Popović6 Author Orcid Image ;   Lars S Maier7 Author Orcid Image ;   Lazar Velicki8, 9 Author Orcid Image ;   Iacopo Olivotto3, 4 Author Orcid Image ;   Guy A MacGowan10 Author Orcid Image ;   Djordje G Jakovljević10, 11 Author Orcid Image ;   Nenad Filipović12 Author Orcid Image ;   Zoran Bosnić1 Author Orcid Image
Article Authors Cited by (9) Tweetations (11) Metrics

Journals

  1. Rajesh E, Basheer S, Dhanaraj R, Yadav S, Kadry S, Khan M, Kim Y, Cha J. Machine Learning for Online Automatic Prediction of Common Disease Attributes Using Never-Ending Image Learner. Diagnostics 2022;13(1):95 View
  2. Mapundu M, Kabudula C, Musenge E, Olago V, Celik T. Performance evaluation of machine learning and Computer Coded Verbal Autopsy (CCVA) algorithms for cause of death determination: A comparative analysis of data from rural South Africa. Frontiers in Public Health 2022;10 View
  3. Tomasevic S, Milosevic M, Milicevic B, Simic V, Prodanovic M, Mijailovich S, Filipovic N. Computational Modeling on Drugs Effects for Left Ventricle in Cardiomyopathy Disease. Pharmaceutics 2023;15(3):793 View
  4. Loeffler S, Trayanova N. Primer on Machine Learning in Electrophysiology. Arrhythmia & Electrophysiology Review 2023;12 View
  5. Kaur S, Desai M. Unmet needs and future directions in hypertrophic cardiomyopathy. Progress in Cardiovascular Diseases 2023;80:1 View
  6. Pezoulas V, Zaridis D, Mylona E, Androutsos C, Apostolidis K, Tachos N, Fotiadis D. Synthetic data generation methods in healthcare: A review on open-source tools and methods. Computational and Structural Biotechnology Journal 2024;23:2892 View
  7. Salih A, Galazzo I, Gkontra P, Rauseo E, Lee A, Lekadir K, Radeva P, Petersen S, Menegaz G. A review of evaluation approaches for explainable AI with applications in cardiology. Artificial Intelligence Review 2024;57(9) View
  8. Zhang J, Wang X, Qiu H, Zhang Y, Han W, Wang J, Wang T, Shi Y, Huang M, Zhuang J, Guo H, Xu X. HOCM-Net: 3D coarse-to-fine structural prior fusion based segmentation network for the surgical planning of hypertrophic obstructive cardiomyopathy. Expert Systems with Applications 2024;257:125005 View
  9. Brahma A, Chatterjee S, Seal K, Fitzpatrick B, Tao Y. Development of a Cohort Analytics Tool for Monitoring Progression Patterns in Cardiovascular Diseases: Advanced Stochastic Modeling Approach. JMIR Medical Informatics 2024;12:e59392 View