Application of Machine Learning Techniques to High-Dimensional Clinical Data to Forecast Postoperative Complications

OBJECTIVE: To compare performance of risk prediction models for forecasting postoperative sepsis and acute kidney injury. DESIGN: Retrospective single center cohort study of adult surgical patients admitted between 2000 and 2010. PATIENTS: 50,318 adult patients undergoing major surgery. MEASUREMENTS...

Descripción completa

Detalles Bibliográficos
Autores principales: Thottakkara, Paul, Ozrazgat-Baslanti, Tezcan, Hupf, Bradley B., Rashidi, Parisa, Pardalos, Panos, Momcilovic, Petar, Bihorac, Azra
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4883761/
https://www.ncbi.nlm.nih.gov/pubmed/27232332
http://dx.doi.org/10.1371/journal.pone.0155705

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4883761/
https://www.ncbi.nlm.nih.gov/pubmed/27232332
http://dx.doi.org/10.1371/journal.pone.0155705

Ejemplares similares