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Application of an exploratory knowledge-discovery pipeline based on machine learning to multi-scale omics data to characterise myocardial injury in a cohort of patients with septic shock: An observational study

par Pinto, Bernardo Bollen;Ripoll, Vicent Ribas;Subías-Beltrán, Paula;Herpain, Antoine ;Barlassina, Cristina;Oliveira, Eliandre;Pastorelli, Roberta;Braga, Daniele;Barcella, Matteo;Subirats, Laia;Bauzá-Martinez, J.;Odena, María Antonia;Ferrario, Manuela;Baselli, Giuseppe;Aletti, Federico;Bendjelid, Karim
Référence Journal of Clinical Medicine, 10, 19, 4354
Publication Publié, 2021-10

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Titre:	Application of an exploratory knowledge-discovery pipeline based on machine learning to multi-scale omics data to characterise myocardial injury in a cohort of patients with septic shock: An observational study
Auteur:	Pinto, Bernardo Bollen; Ripoll, Vicent Ribas; Subías-Beltrán, Paula; Herpain, Antoine; Barlassina, Cristina; Oliveira, Eliandre; Pastorelli, Roberta; Braga, Daniele; Barcella, Matteo; Subirats, Laia; Bauzá-Martinez, J.; Odena, María Antonia; Ferrario, Manuela; Baselli, Giuseppe; Aletti, Federico; Bendjelid, Karim
Informations sur la publication:	Journal of Clinical Medicine, 10, 19, 4354
Statut de publication:	Publié, 2021-10
Sujet CREF:	Médecine clinique [biologie clinique]
Mots-clés:	Feature selection
	Myocardial injury
	Septic cardiomyopathy
	Septic shock machine learning
Note générale:	SCOPUS: ar.j
Langue:	Anglais
Identificateurs:	urn:issn:2077-0383
	info:doi/10.3390/jcm10194354
	info:scp/85115605076