The use of clustering algorithms in critical care research to unravel patient heterogeneity

doi:10.1007/s00134-019-05631-z

Editorial

. 2019 Jul;45(7):1025-1028.

doi: 10.1007/s00134-019-05631-z. Epub 2019 May 6.

The use of clustering algorithms in critical care research to unravel patient heterogeneity

José Castela Forte^{1

2}, Anders Perner³, Iwan C C van der Horst⁴

Affiliations

¹ Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, The Netherlands.
² Department of Critical Care, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
³ Department of Intensive Care, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.
⁴ Department of Critical Care, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. i.c.c.van.der.horst@umcg.nl.

PMID: 31062051
DOI: 10.1007/s00134-019-05631-z

Editorial

The use of clustering algorithms in critical care research to unravel patient heterogeneity

José Castela Forte et al. Intensive Care Med. 2019 Jul.

. 2019 Jul;45(7):1025-1028.

doi: 10.1007/s00134-019-05631-z. Epub 2019 May 6.

Authors

José Castela Forte^{1

2}, Anders Perner³, Iwan C C van der Horst⁴

Affiliations

¹ Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, The Netherlands.
² Department of Critical Care, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
³ Department of Intensive Care, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.
⁴ Department of Critical Care, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. i.c.c.van.der.horst@umcg.nl.

PMID: 31062051
DOI: 10.1007/s00134-019-05631-z

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Cardiovascular clusters in septic shock combining clinical and echocardiographic parameters: a post hoc analysis.
Geri G, Vignon P, Aubry A, Fedou AL, Charron C, Silva S, Repessé X, Vieillard-Baron A. Geri G, et al. Intensive Care Med. 2019 May;45(5):657-667. doi: 10.1007/s00134-019-05596-z. Epub 2019 Mar 19. Intensive Care Med. 2019. PMID: 30888443

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References

1. Crit Care Med. 2017 Oct;45(10):1607-1615 - PubMed
1. Stat Med. 2017 Feb 20;36(4):671-686 - PubMed
1. Bioinformatics. 2005 Aug 1;21(15):3201-12 - PubMed
1. Lancet Respir Med. 2018 Sep;6(9):691-698 - PubMed
1. Intensive Care Med. 2019 May;45(5):657-667 - PubMed

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[1] Crit Care Med. 2017 Oct;45(10):1607-1615 - PubMed

[2] Crit Care Med. 2017 Oct;45(10):1607-1615 - PubMed

[3] Stat Med. 2017 Feb 20;36(4):671-686 - PubMed

[4] Stat Med. 2017 Feb 20;36(4):671-686 - PubMed

[5] Bioinformatics. 2005 Aug 1;21(15):3201-12 - PubMed

[6] Bioinformatics. 2005 Aug 1;21(15):3201-12 - PubMed

[7] Lancet Respir Med. 2018 Sep;6(9):691-698 - PubMed

[8] Lancet Respir Med. 2018 Sep;6(9):691-698 - PubMed

[9] Intensive Care Med. 2019 May;45(5):657-667 - PubMed

[10] Intensive Care Med. 2019 May;45(5):657-667 - PubMed

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The use of clustering algorithms in critical care research to unravel patient heterogeneity

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The use of clustering algorithms in critical care research to unravel patient heterogeneity

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