Use of Multi-Modal Data and Machine Learning to Improve Cardiovascular Disease Care.
Artificial Intelligence
big data
cardiovascular risk factors
cardiovascular risk prediction
learning health care system
machine learning
Journal
Frontiers in cardiovascular medicine
ISSN: 2297-055X
Titre abrégé: Front Cardiovasc Med
Pays: Switzerland
ID NLM: 101653388
Informations de publication
Date de publication:
2022
2022
Historique:
received:
20
12
2021
accepted:
21
03
2022
entrez:
16
5
2022
pubmed:
17
5
2022
medline:
17
5
2022
Statut:
epublish
Résumé
Today's digital health revolution aims to improve the efficiency of healthcare delivery and make care more personalized and timely. Sources of data for digital health tools include multiple modalities such as electronic medical records (EMR), radiology images, and genetic repositories, to name a few. While historically, these data were utilized in silos, new machine learning (ML) and deep learning (DL) technologies enable the integration of these data sources to produce multi-modal insights. Data fusion, which integrates data from multiple modalities using ML and DL techniques, has been of growing interest in its application to medicine. In this paper, we review the state-of-the-art research that focuses on how the latest techniques in data fusion are providing scientific and clinical insights specific to the field of cardiovascular medicine. With these new data fusion capabilities, clinicians and researchers alike will advance the diagnosis and treatment of cardiovascular diseases (CVD) to deliver more timely, accurate, and precise patient care.
Identifiants
pubmed: 35571171
doi: 10.3389/fcvm.2022.840262
pmc: PMC9091962
doi:
Types de publication
Journal Article
Review
Langues
eng
Pagination
840262Subventions
Organisme : NHLBI NIH HHS
ID : R25 HL145817
Pays : United States
Informations de copyright
Copyright © 2022 Amal, Safarnejad, Omiye, Ghanzouri, Cabot and Ross.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Références
Physiol Meas. 2020 Dec 09;41(11):
pubmed: 33080588
J Am Heart Assoc. 2020 Feb 18;9(4):e013924
pubmed: 32067584
Heart Fail Rev. 2020 May;25(3):427-446
pubmed: 31792657
Int J Gen Med. 2018 Apr 09;11:127-141
pubmed: 29670391
Circulation. 2014 Jun 24;129(25 Suppl 2):S49-73
pubmed: 24222018
BMC Med Res Methodol. 2021 Apr 2;21(1):63
pubmed: 33810787
Sci Rep. 2020 Dec 17;10(1):22147
pubmed: 33335111
Inf Fusion. 2020 Dec;64:149-187
pubmed: 32834795
Nature. 2018 Oct;562(7726):203-209
pubmed: 30305743
Circulation. 2017 Apr 4;135(14):e826-e857
pubmed: 28254835
J Am Coll Cardiol. 2020 Dec 22;76(25):2982-3021
pubmed: 33309175
Sci Rep. 2019 Jan 24;9(1):717
pubmed: 30679510
Circulation. 1998 May 12;97(18):1837-47
pubmed: 9603539
Radiol Cardiothorac Imaging. 2020 Apr 16;2(2):e190116
pubmed: 33778554
Am J Cardiol. 2002 Aug 1;90(3):259-67
pubmed: 12173582
Neural Comput. 2020 May;32(5):829-864
pubmed: 32186998
Circulation. 2015 Jan 20;131(3):269-79
pubmed: 25398313
Health Technol Assess. 2008 May;12(17):iii-iv, ix-143
pubmed: 18462576
J Am Heart Assoc. 2021 Dec 7;10(23):e021976
pubmed: 34845917
J Clin Epidemiol. 2016 Feb;70:214-23
pubmed: 26441289
Methodist Debakey Cardiovasc J. 2014 Jul-Sep;10(3):139-45
pubmed: 25574340
Health Aff (Millwood). 2007 Jan-Feb;26(1):38-48
pubmed: 17211012
N Engl J Med. 2019 Aug 15;381(7):668-676
pubmed: 31412182