Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial.

Shimabukuro, David W; Barton, Christopher W; Feldman, Mitchell D; Mataraso, Samson J; Das, Ritankar

Shimabukuro, David W; Barton, Christopher W; Feldman, Mitchell D; Mataraso, Samson J; Das, Ritankar.

Afiliación

Shimabukuro DW; Division of Critical Care Medicine, Department of Anesthesia and Perioperative Care, University of California San Francisco, San Francisco, California, USA.
Barton CW; Department of Emergency Medicine, University of California San Francisco, San Francisco, California, USA.
Feldman MD; Division of General Internal Medicine, Department of Medicine, University of California San Francisco, San Francisco, California, USA.
Mataraso SJ; Department of Bioengineering, University of California Berkeley, Berkeley, California, USA.
Das R; Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, USA.

BMJ Open Respir Res ; 4(1): e000234, 2017.

Article en En | MEDLINE | ID: mdl-29435343

Palabras clave

alerts; electronic health records; machine learning; patient monitoring; prediction; sepsis; severe sepsis

Texto completo

Imprimir

XML

PubMed Links

Buscar en Google

Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Clinical_trials / Prognostic_studies / Risk_factors_studies Idioma: En Revista: BMJ Open Respir Res Año: 2017 Tipo del documento: Article

Texto completo

Imprimir

XML

PubMed Links

Buscar en Google