The Use of Deep Learning in the Diagnosis and Prediction of Heart Failure: A scoping review-Reference-Cited by-同舟云学术

The Use of Deep Learning in the Diagnosis and Prediction of Heart Failure: A scoping review

Published:2024-05-17 Issue: Volume: Page:186-192
ISSN:
Container-title:Proceedings of the 2024 8th International Conference on Medical and Health Informatics
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Author:

Alsaify Abdel Rahman¹^ORCID,Siam Aisha²^ORCID,Hassan Hudhaifa²^ORCID,Alzubaidi Mahmood²^ORCID,Househ Mowafa²^ORCID

Affiliation:

1. College of Science and Engineering, Hamad Bin Khalifa University, Qatar and National Center for Cancer Care and Research, Hamad Medical Corporation, Qatar

2. College of Science and Engineering, Hamad Bin Khalifa University, Qatar

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3673971.3673973

Reference19 articles.

1. Castiglione V. Aimo A. Vergaro G. Saccaro L. Passino C. and Emdin M. 2022. Biomarkers for the diagnosis and management of heart failure. Heart failure reviews pp.1-19.

2. Intelligent Framework for Prediction of Heart Disease using Deep Learning

3. Rasmya L Wu Y Wang N Geng X Zheng WJ Wang F Wu H Xu H Zhi D. A study of generalizability of recurrent neural network-based predictive models for heart failure onset risk using a large and heterogeneous EHR data set. J Biomed Inform. 2018 Aug;84:11-16. doi: 10.1016/j.jbi.2018.06.011. Epub 2018 Jun 15. PMID: 29908902; PMCID: PMC6076336.Sam Anzaroot and Andrew McCallum. 2013. UMass Citation Field Extraction Dataset. Retrieved May 27 2019 from http://www.iesl.cs.umass.edu/data/data-umasscitationfield

4. Ngo, L.H. (2019). Using a Deep Learning Network to Diagnose Congestive Heart Failure. Radiology, 290 2, 523-524Chelsea Finn. 2018. Learning to Learn with Gradients. PhD Thesis, EECS Department, University of Berkeley.

5. Bian P Zhang X Liu R Li H Zhang Q Dai B. Deep-Learning-Based Color Doppler Ultrasound Image Feature in the Diagnosis of Elderly Patients with Chronic Heart Failure Complicated with Sarcopenia. J Healthc Eng. 2021 Jul 29;2021:2603842. doi: 10.1155/2021/2603842. PMID: 34367535; PMCID: PMC8346313.