Unfolding physiological state: mortality modelling in intensive care units
Accurate knowledge of a patient's disease state and trajectory is critical in a clinical setting. Modern electronic healthcare records contain an increasingly large amount of data, and the ability to automatically identify the factors that influence patient outcomes stand to greatly improve the...
Main Authors: | Ghassemi, Marzyeh, Doshi-Velez, Finale, Joshi, Rohit, Rumshisky, Anna, Szolovits, Peter, Naumann, Tristan, Brimmer, Nicole J. |
---|---|
Other Authors: | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
Format: | Article |
Language: | en_US |
Published: |
Association for Computing Machinery (ACM)
2016
|
Online Access: | http://hdl.handle.net/1721.1/101049 https://orcid.org/0000-0001-6349-7251 https://orcid.org/0000-0003-2150-1747 https://orcid.org/0000-0001-8411-6403 |
Similar Items
-
Predicting early psychiatric readmission with natural language processing of narrative discharge summaries
by: Castro, V M, et al.
Published: (2017) -
Prediction using patient comparison vs. modeling: A case study for mortality prediction
by: Hoogendoorn, Mark, et al.
Published: (2017) -
Semi-supervised biomedical translation with cycle Wasserstein regression GaNs
by: McDermott, Matthew, et al.
Published: (2020) -
Bayesian nonparametric approaches for reinforcement learning in partially observable domains
by: Doshi-Velez, Finale
Published: (2012) -
A multivariate timeseries modeling approach to severity of illness assessment and forecasting in ICU with sparse, heterogeneous clinical data
by: Pimentel, Marco A. F., et al.
Published: (2017)