Predicting intervention onset in the ICU with switching state space models

The impact of many intensive care unit interventions has not been fully quantified, especially in heterogeneous patient populations. We train unsupervised switching state autoregressive models on vital signs from the public MIMIC-III database to capture patient movement between physiological states....

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Detalles Bibliográficos
Autores principales: Ghassemi, Marzyeh, Wu, Mike, Hughes, Michael C., Szolovits, Peter, Doshi-Velez, Finale
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Medical Informatics Association 2017
Materias:
Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5543372/
https://www.ncbi.nlm.nih.gov/pubmed/28815112

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5543372/
https://www.ncbi.nlm.nih.gov/pubmed/28815112

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