scholarly journals Correction: Development of a Real-Time Risk Prediction Model for In-Hospital Cardiac Arrest in Critically Ill Patients Using Deep Learning: Retrospective Study (Preprint)

2020 ◽  
Author(s):  
Yu Rang Park
Keyword(s):  
Cardiac Arrest ◽  
Deep Learning ◽  
Retrospective Study ◽  
Prediction Model ◽  
Real Time ◽  
Critically Ill ◽  
Risk Prediction ◽  
Critically Ill Patients ◽  
Risk Prediction Model ◽  
Hospital Cardiac Arrest

scholarly journals Development of a Real-Time Risk Prediction Model for In-Hospital Cardiac Arrest in Critically Ill Patients Using Deep Learning: Retrospective Study (Preprint)

2019 ◽  
Author(s):  
Junetae Kim ◽  
Yu Rang Park ◽  
Jeong Hoon Lee ◽  
Jae-Ho Lee ◽  
Young-Hak Kim ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Intensive Care ◽  
Deep Learning ◽  
Retrospective Study ◽  
Prediction Model ◽  
Real Time ◽  
Risk Prediction ◽  
Time Dependent ◽  
Time Prediction ◽  
Deep Learning Model

BACKGROUND Cardiac arrest is the most serious death-related event in intensive care units (ICUs), but it is not easily predicted because of the complex and time-dependent data characteristics of intensive care patients. Given the complexity and time dependence of ICU data, deep learning–based methods are expected to provide a good foundation for developing risk prediction models based on large clinical records. OBJECTIVE This study aimed to implement a deep learning model that estimates the distribution of cardiac arrest risk probability over time based on clinical data and assesses its potential. METHODS A retrospective study of 759 ICU patients was conducted between January 2013 and July 2015. A character-level gated recurrent unit with a Weibull distribution algorithm was used to develop a real-time prediction model. Fivefold cross-validation testing (training set: 80% and validation set: 20%) determined the consistency of model accuracy. The time-dependent area under the curve (TAUC) was analyzed based on the aggregation of 5 validation sets. RESULTS The TAUCs of the implemented model were 0.963, 0.942, 0.917, 0.875, 0.850, 0.842, and 0.761 before cardiac arrest at 1, 8, 16, 24, 32, 40, and 48 hours, respectively. The sensitivity was between 0.846 and 0.909, and specificity was between 0.923 and 0.946. The distribution of risk between the cardiac arrest group and the non–cardiac arrest group was generally different, and the difference rapidly increased as the time left until cardiac arrest reduced. CONCLUSIONS A deep learning model for forecasting cardiac arrest was implemented and tested by considering the cumulative and fluctuating effects of time-dependent clinical data gathered from a large medical center. This real-time prediction model is expected to improve patient’s care by allowing early intervention in patients at high risk of unexpected cardiac arrests.

scholarly journals Development of a Real-Time Risk Prediction Model for In-Hospital Cardiac Arrest in Critically Ill Patients Using Deep Learning: Retrospective Study

10.2196/16349 ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. e16349 ◽  
Author(s):  
Junetae Kim ◽  
Yu Rang Park ◽  
Jeong Hoon Lee ◽  
Jae-Ho Lee ◽  
Young-Hak Kim ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Intensive Care ◽  
Deep Learning ◽  
Retrospective Study ◽  
Prediction Model ◽  
Real Time ◽  
Risk Prediction ◽  
Time Dependent ◽  
Time Prediction ◽  
Deep Learning Model

Background Cardiac arrest is the most serious death-related event in intensive care units (ICUs), but it is not easily predicted because of the complex and time-dependent data characteristics of intensive care patients. Given the complexity and time dependence of ICU data, deep learning–based methods are expected to provide a good foundation for developing risk prediction models based on large clinical records. Objective This study aimed to implement a deep learning model that estimates the distribution of cardiac arrest risk probability over time based on clinical data and assesses its potential. Methods A retrospective study of 759 ICU patients was conducted between January 2013 and July 2015. A character-level gated recurrent unit with a Weibull distribution algorithm was used to develop a real-time prediction model. Fivefold cross-validation testing (training set: 80% and validation set: 20%) determined the consistency of model accuracy. The time-dependent area under the curve (TAUC) was analyzed based on the aggregation of 5 validation sets. Results The TAUCs of the implemented model were 0.963, 0.942, 0.917, 0.875, 0.850, 0.842, and 0.761 before cardiac arrest at 1, 8, 16, 24, 32, 40, and 48 hours, respectively. The sensitivity was between 0.846 and 0.909, and specificity was between 0.923 and 0.946. The distribution of risk between the cardiac arrest group and the non–cardiac arrest group was generally different, and the difference rapidly increased as the time left until cardiac arrest reduced. Conclusions A deep learning model for forecasting cardiac arrest was implemented and tested by considering the cumulative and fluctuating effects of time-dependent clinical data gathered from a large medical center. This real-time prediction model is expected to improve patient’s care by allowing early intervention in patients at high risk of unexpected cardiac arrests.

A Real-time Risk-Prediction Model for Pediatric Venous Thromboembolic Events

PEDIATRICS ◽  
2021 ◽  
pp. e2020042325
Author(s):  
Shannon C. Walker ◽  
C. Buddy Creech ◽  
Henry J. Domenico ◽  
Benjamin French ◽  
Daniel W. Byrne ◽  
...  
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Thromboembolic Events ◽  
Venous Thromboembolic Events ◽  
Venous Thromboembolic

scholarly journals Survival After In-Hospital Cardiac Arrest in Critically Ill Patients

2020 ◽  
Vol 13 (7) ◽  
Author(s):  
Saket Girotra ◽  
Yuanyuan Tang ◽  
Paul S. Chan ◽  
Brahmajee K. Nallamothu ◽  
Keyword(s):  
Cardiac Arrest ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Hospital Cardiac Arrest
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