Predicting the Need For Vasopressors in the Intensive Care Unit Using an Attention Based Deep Learning Model.
Shock
; 56(1): 73-79, 2021 07 01.
Article
en En
| MEDLINE
| ID: mdl-33177372
ABSTRACT
BACKGROUND:
Previous models on prediction of shock mostly focused on septic shock and often required laboratory results in their models. The purpose of this study was to use deep learning approaches to predict vasopressor requirement for critically ill patients within 24âh of intensive care unit (ICU) admission using only vital signs.METHODS:
We used data from the Medical Information Mart for Intensive Care III database and the eICU Collaborative Research Database to develop a vasopressor prediction model. We performed systematic data preprocessing using matching of cohorts, oversampling, and imputation to control for bias, class imbalance, and missing data. Bidirectional long short-term memory (Bi-LSTM), a multivariate time series model, was used to predict the need for vasopressor therapy using serial physiological data collected 21âh prior to prediction time.RESULTS:
Using data from 10,941 critically ill patients from 209 ICUs, our model achieved an initial area under the curve of 0.96 (95% CI 0.96-0.96) to predict the need for vasopressor therapy in 2âh within the first day of ICU admission. After matching to control class imbalance, the Bi-LSTM model had area under the curve of 0.83 (95% CI 0.82-0.83). Heart rate, respiratory rate, and mean arterial pressure contributed most to the model.CONCLUSIONS:
We used Bi-LSTM to develop a model to predict the need for vasopressor for critically ill patients for the first 24âh of ICU admission. With attention mechanism, respiratory rate, mean arterial pressure, and heart rate were identified as key sequential determinants of vasopressor requirements.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Vasoconstrictores
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Enfermedad Crítica
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Evaluación de Necesidades
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Aprendizaje Profundo
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Unidades de Cuidados Intensivos
Tipo de estudio:
Etiology_studies
/
Incidence_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Aged
/
Female
/
Humans
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Male
/
Middle aged
Idioma:
En
Revista:
Shock
Asunto de la revista:
ANGIOLOGIA
/
CARDIOLOGIA
Año:
2021
Tipo del documento:
Article
País de afiliación:
China