ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU

To improve the performance of Intensive Care Units (ICUs), the field of bio-statistics has developed scores which try to predict the likelihood of negative outcomes. These help evaluate the effectiveness of treatments and clinical practice, and also help to identify patients with unexpected outcomes...

Full description

Autores:

Tipo de recurso:

Fecha de publicación:

2019

Institución:

Universidad Tecnológica de Bolívar

Repositorio:

Repositorio Institucional UTB

Idioma:

eng

OAI Identifier:

oai:repositorio.utb.edu.co:20.500.12585/8995

Acceso en línea:

https://hdl.handle.net/20.500.12585/8995

Palabra clave:

Deep learning
ICU
MIMIC-III
Shapley values
Deep learning
Forecasting
Game theory
Intensive care units
Clinical practices
Coalitional game theory
Medical information
MIMIC-III
Relevant features
Shapley value
Sub-optimal performance
Traditional techniques
Deep neural networks
Adoption
Article
Deep learning
Game
Human
Intensive care unit
Medical information
Mortality
Prediction
Deep neural network

Rights

restrictedAccess

License

http://creativecommons.org/licenses/by-nc-nd/4.0/