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
Contributeurs/tricesBollen Pinto, Bernardo![orcid](assets/images/orcid.png)
; Ribas Ripoll, Vicent; Subías-Beltrán, Paula![orcid](assets/images/orcid.png)
; Herpain, Antoine![orcid](assets/images/orcid.png)
; Barlassina, Cristina; Oliveira, Eliandre; Pastorelli, Roberta; Braga, Daniele![orcid](assets/images/orcid.png)
; Barcella, Matteo; Subirats, Laia![orcid](assets/images/orcid.png)
; Bauzá-Martinez, Julia; Odena, Antonia; Ferrario, Manuela![orcid](assets/images/orcid.png)
; Baselli, Giuseppe![orcid](assets/images/orcid.png)
; Aletti, Federico; Bendjelid, Karim![orcid](assets/images/orcid.png)
; Shockomics Consortium
![orcid](assets/images/orcid.png)
![orcid](assets/images/orcid.png)
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![orcid](assets/images/orcid.png)
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Publié dansJournal of clinical medicine, vol. 10, no. 19, 4354
Date de publication2021
Date de mise en ligne2021-09-24
Résumé
Mots-clés
- Feature selection
- Machine learning
- Myocardial injury
- Septic cardiomyopathy
- Septic shock
Structure d'affiliation
Groupe de recherche
Financement
- European Commission - MULTISCALE APPROACH TO THE IDENTIFICATION OF MOLECULAR BIOMARKERS IN ACUTE HEART FAILURE INDUCED BY SHOCK [602706]
Citation (format ISO)
BOLLEN PINTO, Bernardo et al. 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. In: Journal of clinical medicine, 2021, vol. 10, n° 19, p. 4354. doi: 10.3390/jcm10194354
Fichiers principaux (1)
Article (Published version)
Fichiers secondaires (1)
Identifiants
- PID : unige:158356
- DOI : 10.3390/jcm10194354
- PMID : 34640372
- PMCID : PMC8509561
URL commercialhttps://www.mdpi.com/2077-0383/10/19/4354
ISSN du journal2077-0383