*Result*: Unsupervised machine learning for cardiovascular disease: A framework for future studies.
Title:
Unsupervised machine learning for cardiovascular disease: A framework for future studies.
Authors:
Bresso E; Université de Lorraine, INSERM, Centre d'Investigations Cliniques Plurithématique 1433, Inserm U1116, CHRU de Nancy and F-CRIN INI-CRCT, Nancy, France., Lacomblez C; Université de Lorraine, INSERM, Centre d'Investigations Cliniques Plurithématique 1433, Inserm U1116, CHRU de Nancy and F-CRIN INI-CRCT, Nancy, France., Duarte K; Université de Lorraine, INSERM, Centre d'Investigations Cliniques Plurithématique 1433, Inserm U1116, CHRU de Nancy and F-CRIN INI-CRCT, Nancy, France., Monzo L; Université de Lorraine, INSERM, Centre d'Investigations Cliniques Plurithématique 1433, Inserm U1116, CHRU de Nancy and F-CRIN INI-CRCT, Nancy, France., Baudry G; Université de Lorraine, INSERM, Centre d'Investigations Cliniques Plurithématique 1433, Inserm U1116, CHRU de Nancy and F-CRIN INI-CRCT, Nancy, France., Tromp J; Saw Swee Hock School of Public Health, National University of Singapore & National University Health System, Singapore, Singapore., Sharma A; Division of Cardiology, McGill University Health Centre, McGill University, Montreal, QC, Canada., Girerd N; Université de Lorraine, INSERM, Centre d'Investigations Cliniques Plurithématique 1433, Inserm U1116, CHRU de Nancy and F-CRIN INI-CRCT, Nancy, France.
Source:
European journal of heart failure [Eur J Heart Fail] 2025 Dec; Vol. 27 (12), pp. 3366-3377. Date of Electronic Publication: 2025 Nov 06.
Publication Type:
Journal Article; Review
Language:
English
Journal Info:
Publisher: Wiley Country of Publication: England NLM ID: 100887595 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-0844 (Electronic) Linking ISSN: 13889842 NLM ISO Abbreviation: Eur J Heart Fail Subsets: MEDLINE
Imprint Name(s):
Publication: 2014- : Chichester : Wiley
Original Publication: Amsterdam ; New York : Elsevier Science, c1999-
Original Publication: Amsterdam ; New York : Elsevier Science, c1999-
MeSH Terms:
References:
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J Cardiovasc Transl Res. 2017 Jun;10(3):275-284. (PMID: 28258421)
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J Multimorb Comorb. 2023 May 19;13:26335565231173845. (PMID: 37223823)
Am J Cardiol. 2023 Jan 15;187:54-61. (PMID: 36459748)
JACC Cardiovasc Imaging. 2022 Feb;15(2):193-208. (PMID: 34538625)
Int J Cardiol. 2021 May 15;331:138-143. (PMID: 33529665)
Biomedicines. 2025 Apr 23;13(5):. (PMID: 40426849)
J Am Coll Cardiol. 2020 Dec 22;76(25):2982-3021. (PMID: 33309175)
Comput Methods Programs Biomed. 2016 Jul;130:54-64. (PMID: 27208521)
Sci Rep. 2021 Feb 18;11(1):4202. (PMID: 33603019)
BMJ. 2023 Jun 14;381:e073843. (PMID: 37315959)
Can J Cardiol. 2024 Oct;40(10):1922-1933. (PMID: 39111729)
J Am Coll Cardiol. 2021 Jan 26;77(3):300-313. (PMID: 33478654)
IEEE Trans Pattern Anal Mach Intell. 2005 Aug;27(8):1226-38. (PMID: 16119262)
Sci Rep. 2020 Sep 29;10(1):16057. (PMID: 32994452)
IEEE Trans Pattern Anal Mach Intell. 1979 Feb;1(2):224-7. (PMID: 21868852)
Int J Cardiol. 2024 Sep 1;410:132195. (PMID: 38782072)
Open Med Inform J. 2010 Jul 27;4:136-40. (PMID: 21589851)
Diabetes Obes Metab. 2024 Nov;26(11):5025-5035. (PMID: 39301712)
JACC Heart Fail. 2021 Aug;9(8):568-577. (PMID: 34325887)
BMJ Open. 2021 Nov 15;11(11):e049334. (PMID: 34782339)
Eur Heart J. 2013 May;34(19):1404-13. (PMID: 23095984)
J Physiol. 2021 Nov;599(22):4991-5013. (PMID: 34510457)
Circ Heart Fail. 2021 Feb;14(2):e006799. (PMID: 33557575)
Can J Cardiol. 2024 Oct;40(10):1774-1787. (PMID: 38825181)
J Am Coll Cardiol. 2018 Jun 12;71(23):2668-2679. (PMID: 29880128)
J Am Heart Assoc. 2018 Apr 12;7(8):. (PMID: 29650709)
JACC Cardiovasc Imaging. 2023 Oct;16(10):1271-1284. (PMID: 37204382)
J Am Soc Echocardiogr. 2019 May;32(5):604-615.e6. (PMID: 30718020)
Int J Cardiol. 2018 Jul 1;262:57-63. (PMID: 29622508)
Nat Med. 2019 Jan;25(1):70-74. (PMID: 30617318)
J Biomed Inform. 2025 Mar;163:104800. (PMID: 39956346)
Comput Methods Programs Biomed. 2015 Dec;122(3):372-83. (PMID: 26387633)
JAMA Cardiol. 2021 Mar 1;6(3):304-313. (PMID: 33295965)
Iran J Public Health. 2020 May;49(5):923-930. (PMID: 32953680)
Front Artif Intell. 2025 Jun 19;8:1576992. (PMID: 40612384)
Circulation. 2015 Jan 20;131(3):269-79. (PMID: 25398313)
PLoS One. 2016 Feb 03;11(2):e0145881. (PMID: 26840410)
Am J Cardiol. 2019 Apr 15;123(8):1193-1201. (PMID: 30739657)
BMC Med. 2019 Oct 29;17(1):195. (PMID: 31665002)
N Engl J Med. 2021 Nov 11;385(20):1845-1855. (PMID: 34758252)
Bioinformatics. 2019 Apr 1;35(7):1255-1257. (PMID: 30192923)
Biomed Res Int. 2020 Feb 18;2020:4281243. (PMID: 32149106)
Diagnostics (Basel). 2024 Jan 08;14(2):. (PMID: 38248021)
Eur J Heart Fail. 2023 Sep;25(9):1507-1525. (PMID: 37560778)
Heart Views. 2024 Oct-Dec;25(4):241-248. (PMID: 40488153)
Eur J Heart Fail. 2025 Dec;27(12):3378-3391. (PMID: 39654426)
Curr Heart Fail Rep. 2021 Oct;18(5):253-263. (PMID: 34427881)
BMC Bioinformatics. 2022 May 31;23(1):205. (PMID: 35641905)
Eur J Clin Invest. 2025 Apr;55 Suppl 1:e70017. (PMID: 40191920)
Eur J Heart Fail. 2023 Oct;25(10):1742-1752. (PMID: 37530453)
Lancet. 2021 Oct 16;398(10309):1427-1435. (PMID: 34474011)
Int J Cardiol. 2023 Jul 15;383:175-184. (PMID: 37119943)
Circulation. 2021 Jun 15;143(24):2370-2383. (PMID: 33845593)
Eur J Heart Fail. 2020 Aug;22(8):1402-1411. (PMID: 31919958)
Nat Med. 2019 Jan;25(1):24-29. (PMID: 30617335)
J Cardiovasc Transl Res. 2017 Jun;10(3):275-284. (PMID: 28258421)
Diagnostics (Basel). 2024 Nov 18;14(22):. (PMID: 39594254)
Int J Cardiol. 2023 May 1;378:71-76. (PMID: 36870449)
PLOS Digit Health. 2023 Sep 13;2(9):e0000334. (PMID: 37703231)
Diabetes Care. 2022 Jan 1;45(1):204-212. (PMID: 34716214)
J Multimorb Comorb. 2023 May 19;13:26335565231173845. (PMID: 37223823)
Am J Cardiol. 2023 Jan 15;187:54-61. (PMID: 36459748)
JACC Cardiovasc Imaging. 2022 Feb;15(2):193-208. (PMID: 34538625)
Int J Cardiol. 2021 May 15;331:138-143. (PMID: 33529665)
Biomedicines. 2025 Apr 23;13(5):. (PMID: 40426849)
J Am Coll Cardiol. 2020 Dec 22;76(25):2982-3021. (PMID: 33309175)
Contributed Indexing:
Keywords: Cardiovascular diseases; Clustering algorithms; Patient stratification; Precision medicine; Unsupervised machine learning
Entry Date(s):
Date Created: 20251106 Date Completed: 20260114 Latest Revision: 20260116
Update Code:
20260130
PubMed Central ID:
PMC12803537
DOI:
10.1002/ejhf.70076
PMID:
41195655
Database:
MEDLINE
*Further Information*
*Unsupervised machine learning can improve the characterization and stratification of patients with cardiovascular diseases (CVDs). Clustering algorithms, which group patients based on patterns in clinical data, can reveal distinct subgroups that may differ in prognosis and treatment response. Despite increasing research in this area, the practical use of clustering methods in routine clinical care remains limited by the lack of accessible tools and rigorous external validation. This review presents a systematic framework for applying unsupervised machine learning techniques to CVD research. The framework outlines a stepwise process-from identifying patient clusters and establishing their associations with clinical outcomes to developing predictive models for assigning new patients to these clusters. This approach aims to generate robust, externally validated models that can be integrated into clinical practice to support improved risk stratification and personalized treatment strategies. This framework can enhance the usefulness of clustering in CVD research, by providing valuable resource for medical professionals, stakeholders, and researchers in exploring more effective strategies for managing CVDs.
(© 2025 The Author(s). European Journal of Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.)*