The Physiological Deep Learner: First application of multitask deep learning to predict hypotension in critically ill patients.

Arterial Pressure Critical Care Critical Illness Deep Learning Humans Hypotension / diagnosis
Critical care Multitask learning RNN Shock hypotension

Journal

Artificial intelligence in medicine
ISSN: 1873-2860
Titre abrégé: Artif Intell Med
Pays: Netherlands
ID NLM: 8915031

Informations de publication

Date de publication:
08 2021
Historique:
received: 20 02 2021
revised: 12 05 2021
accepted: 21 05 2021
entrez: 20 8 2021
pubmed: 21 8 2021
medline: 9 9 2021
Statut: ppublish

Résumé

Critical care clinicians are trained to analyze simultaneously multiple physiological parameters to predict critical conditions such as hemodynamic instability. We developed the Multi-task Learning Physiological Deep Learner (MTL-PDL), a deep learning algorithm that predicts simultaneously the mean arterial pressure (MAP) and the heart rate (HR). In an external validation dataset, our model exhibited very good calibration: R

Identifiants

DOI: 10.1016/j.artmed.2021.102118 PMID: 34412841
pubmed: 34412841
pii: S0933-3657(21)00111-1
doi: 10.1016/j.artmed.2021.102118
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

102118

Informations de copyright

Copyright © 2021 Elsevier B.V. All rights reserved.

Auteurs

Ményssa Cherifa (M)

Université de Paris, ECSTRRA team, Center of research in epidemiology and statistics (CRESS) - INSERM UMR 1153, 1 Parvis Notre-Dame - Pl. Jean-Paul II, Paris 75004, France.

Yannet Interian (Y)

Data Analytic Program, University of California San Francisco, 101 Howard St, San Francisco, 94105, CA, United States.

Alice Blet (A)

Department of Anesthesiology, Critical Care and Burn Center, Lariboisière-Saint-Louis Hospital, AP-HP Nord, 1 Avenue Claude Vellefaux, Paris 75010, France; Université de Paris, Cardiovascular MArkers in Stress Conditions (MASCOT) - INSERM UMR-S 942, 41, boulevard de la Chapelle, Paris 75010, France; University of Ottawa Heart Institute, University of Ottawa, 40 Ruskin St, Ottawa, Ontario, Canada.

Matthieu Resche-Rigon (M)

Université de Paris, ECSTRRA team, Center of research in epidemiology and statistics (CRESS) - INSERM UMR 1153, 1 Parvis Notre-Dame - Pl. Jean-Paul II, Paris 75004, France; Department of biostastistics and medical information, Lariboisière-Saint-Louis Hospital, AP-HP Nord, 1 Avenue Claude Vellefaux, Paris 75010, France.

Romain Pirracchio (R)

Université de Paris, ECSTRRA team, Center of research in epidemiology and statistics (CRESS) - INSERM UMR 1153, 1 Parvis Notre-Dame - Pl. Jean-Paul II, Paris 75004, France; Department of Anesthesia and Perioperative Medicine, Zuckerberg San Francisco General Hospital and Trauma Center, University of California San Francisco, 1001 Potrero Ave, San Francisco, 94110, CA, United States. Electronic address: romain.pirracchio@gmail.com.

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Classifications MeSH

questionsmedicales.fr › Phénomènes physiologiques respiratoires et circulatoires › Phénomènes physiologiques cardiovasculaires › Hémodynamique › Pression sanguine › Pression artérielle › The Physiological Deep Learner: First...
questionsmedicales.fr › Établissements, main d'oeuvre et services de soins de santé › Services de santé › Soins aux patients › Soins de réanimation › The Physiological Deep Learner: First...
questionsmedicales.fr › États, signes et symptômes pathologiques › Processus pathologiques › Caractéristiques de la maladie › Maladie grave › The Physiological Deep Learner: First...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › Réseaux neuronaux (ordinateur) › Apprentissage profond › The Physiological Deep Learner: First...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › The Physiological Deep Learner: First...
questionsmedicales.fr › Maladies cardiovasculaires › Maladies vasculaires › Hypotension artérielle › The Physiological Deep Learner: First...