Machine learning to predict vasopressin responsiveness in patients with septic shock.

Scheibner, Aileen; Betthauser, Kevin D; Bewley, Alice F; Juang, Paul; Lizza, Bryan; Micek, Scott; Lyons, Patrick G

Scheibner, Aileen; Betthauser, Kevin D; Bewley, Alice F; Juang, Paul; Lizza, Bryan; Micek, Scott; Lyons, Patrick G.

Afiliação

Scheibner A; Department of Pharmacy, Barnes-Jewish Hospital, St. Louis, Missouri, USA.
Betthauser KD; Department of Pharmacy, Barnes-Jewish Hospital, St. Louis, Missouri, USA.
Bewley AF; Division of Pulmonary and Critical Care Medicine, Washington University School of Medicine, St. Louis, Missouri, USA.
Juang P; Department of Pharmacy, Barnes-Jewish Hospital, St. Louis, Missouri, USA.
Lizza B; Department of Pharmacy Practice, University of Health Sciences and Pharmacy, St. Louis, Missouri, USA.
Micek S; Department of Pharmacy, Barnes-Jewish Hospital, St. Louis, Missouri, USA.
Lyons PG; Department of Pharmacy Practice, University of Health Sciences and Pharmacy, St. Louis, Missouri, USA.

Pharmacotherapy ; 42(6): 460-471, 2022 06.

Article em En | MEDLINE | ID: mdl-35426141

Assuntos

Choque Séptico; Adulto; Humanos; Aprendizado de Máquina; Norepinefrina/uso terapêutico; Estudos Retrospectivos; Choque Séptico/tratamento farmacológico; Vasoconstritores/uso terapêutico; Vasopressinas/uso terapêutico

Palavras-chave

artificial intelligence; pharmacology; predictive modeling; septic shock; vasopressors

Texto completo

Adicionar na Minha BVS

Imprimir

XML

PubMed Links

Buscar no Google

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Choque Séptico Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Adult / Humans Idioma: En Revista: Pharmacotherapy Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo

Adicionar na Minha BVS

Imprimir

XML

PubMed Links

Buscar no Google