Machine learning as a supportive tool to recognize cardiac arrest in emergency calls.

Blomberg, Stig Nikolaj; Folke, Fredrik; Ersbøll, Annette Kjær; Christensen, Helle Collatz; Torp-Pedersen, Christian; Sayre, Michael R; Counts, Catherine R; Lippert, Freddy K

Blomberg, Stig Nikolaj; Folke, Fredrik; Ersbøll, Annette Kjær; Christensen, Helle Collatz; Torp-Pedersen, Christian; Sayre, Michael R; Counts, Catherine R; Lippert, Freddy K.

Afiliação

Blomberg SN; Emergency Medical Services Copenhagen, Denmark; Department of Clinical Medicine, University of Copenhagen, Denmark. Electronic address: Stig.Nikolaj.Fasmer.Blomberg@regionh.dk.
Folke F; Emergency Medical Services Copenhagen, Denmark; Department of Clinical Medicine, University of Copenhagen, Denmark; Department of Cardiology, Gentofte University Hospital, Denmark.
Ersbøll AK; National Institute of Public Health, University of Southern Denmark, Denmark.
Christensen HC; Emergency Medical Services Copenhagen, Denmark.
Torp-Pedersen C; Department of Clinical Epidemiology, Aalborg University Hospital, Denmark; Department of Health Science and Technology, Aalborg University, Denmark.
Sayre MR; Department of Emergency Medicine, University of Washington, United States.
Counts CR; Department of Emergency Medicine, University of Washington, United States.
Lippert FK; Emergency Medical Services Copenhagen, Denmark; Department of Clinical Medicine, University of Copenhagen, Denmark.

Resuscitation ; 138: 322-329, 2019 05.

Article em En | MEDLINE | ID: mdl-30664917

Assuntos

Reanimação Cardiopulmonar/métodos; Despacho de Emergência Médica/organização & administração; Sistemas de Comunicação entre Serviços de Emergência; Serviços Médicos de Emergência/métodos; Aprendizado de Máquina; Parada Cardíaca Extra-Hospitalar/diagnóstico; Idoso; Idoso de 80 Anos ou mais; Feminino; Humanos; Masculino; Pessoa de Meia-Idade; Parada Cardíaca Extra-Hospitalar/terapia; Curva ROC; Estudos Retrospectivos; Fatores de Tempo

Palavras-chave

Artificial intelligence; Cardiopulmonary resuscitation; Detection time; Dispatch-assisted cardiopulmonary resuscitation; Emergency medical services; Machine learning; Out-of-hospital cardiac arrest

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Reanimação Cardiopulmonar / Sistemas de Comunicação entre Serviços de Emergência / Serviços Médicos de Emergência / Parada Cardíaca Extra-Hospitalar / Aprendizado de Máquina / Despacho de Emergência Médica Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Aged / Aged80 / Female / Humans / Male / Middle aged Idioma: En Revista: Resuscitation Ano de publicação: 2019 Tipo de documento: Article País de publicação: Irlanda

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