A Database-driven decision support system: customized mortality prediction
We hypothesize that local customized modeling will provide more accurate mortality prediction than the current standard approach using existing scoring systems. Mortality prediction models were developed for two subsets of patients in Multi-parameter Intelligent Monitoring for Intensive Care (MIMIC)...
Main Authors: | Celi, Leo Anthony G., Galvin, Sean, Davidzon, Guido, Lee, Joon, Scott, Daniel, Mark, Roger Greenwood |
---|---|
Outros autores: | Massachusetts Institute of Technology. Institute for Medical Engineering & Science |
Formato: | Artigo |
Idioma: | en_US |
Publicado: |
MDPI AG
2013
|
Acceso en liña: | http://hdl.handle.net/1721.1/77628 https://orcid.org/0000-0001-8593-9321 https://orcid.org/0000-0002-6318-2978 |
Títulos similares
-
Collective Experience: A Database-Fuelled, Inter-Disciplinary Team-Led Learning System
por: Celi, Leo Anthony G., et al.
Publicado: (2012) -
Using EMR transactional data for personalize clinical decision support
por: Davidzon, Guido Alejandro
Publicado: (2010) -
Localized customized mortality prediction modeling for patients with acute kidney injury admitted to the intensive care unit
por: Celi, Leo Anthony G
Publicado: (2010) -
Predicting In-Hospital Mortality of ICU Patients: The PhysioNet/Computing in Cardiology Challenge 2012
por: Silva, Ikaro, et al.
Publicado: (2015) -
Open-access MIMIC-II database for intensive care research
por: Lee, Joon, et al.
Publicado: (2013)