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 Simulated Prospective Evaluation of a Deep Learning Model for Real-Time Prediction of Clinical Deterioration Among Ward Patients

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Parth K. Shah ◽  
Jennifer C. Ginestra ◽  
Lyle H. Ungar ◽  
Paul Junker ◽  
Jeff I. Rohrbach ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Learning Model ◽  
Clinical Deterioration ◽  
Prospective Evaluation ◽  
Time Prediction ◽  
Deep Learning Model

scholarly journals Real-Time Prediction of AKI Among Middle-Aged and Older in ICU: A Retrospective and Machine Learning Study

2020 ◽  
Author(s):  
Hui Wang ◽  
Fuxing Deng ◽  
Buyao Zhang ◽  
Shuangping Zhao
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Deep Learning ◽  
Real Time ◽  
Older Patients ◽  
Support Vector ◽  
Research Database ◽  
Middle Aged ◽  
Data Set ◽  
Time Prediction

Abstract BackgroundAcute Kidney Injury (AKI), a major public health problem，is responsible for two-thirds of intensive care unit patients’ cost, and aging is an independent risk factor for AKI and its associated mortality and morbidity. The early recognition of AKI helps ICU caregivers to guide fluid treatment and titrate the dosing of the nephrotoxic drug. Therefore, it is desirable to build models to predict their position. The study is to build models based machine learning to predict AKI stage after 24 hours and 48 hours among middle-aged and older patients respectively in ICU. Methods and FindingsWe used two real-world databases to build and test models. The Medical Information Mart for Intensive Care (MIMIC-III v1.4) database for training, funded by National Institutes of Health (NIIH) built by the Computational Physiology Laboratory of MIT, Beth Israel Dikon Medical Center, and Philips Medical. The eICU Collaborative Research Database (eICU-CRD v 2.0) for the test is open-access, de-identified data sets of patients admitted to ICUs. 26316 patients in the overall cohort were generally older (median age ranging from 57 to 79) and 54% were male. Here we present three models, using the support vector machine (SVM), Long short-term memory (LSTM), and convolutional LSTM ConvLSTM respectively. the ConvLSTM model had the best performance in the test data set, and it has good ability and surpasses any previous model to predict whether older patients have AKI or not. The area under the receiver operating characteristic curve (AUC) of 24-hour prediction AKI is 99.79%, 48-hours AKI 99.43% during the hospital. we demonstrate that deep learning can handle lots of variables which may be predictors and that the algorithm achieved robust and excellent performance.ConclusionsTo our knowledge, this study is the first to use large-scale data collected from electronic health record（EHR）to prove the contribution of big data and deep learning methods to the real-time prediction of AKI prognosis in middle-aged and elderly patients. The model performance is better than any previous models. This work provides novel evidence to change clinical practice and precise personalized interventions.

The input vector space optimization for LSTM deep learning model in real-time prediction of ship motions

2020 ◽  
Vol 213 ◽  
pp. 107681
Author(s):  
Yucheng Liu ◽  
Wenyang Duan ◽  
Limin Huang ◽  
Shiliang Duan ◽  
Xuewen Ma
Keyword(s):  
Deep Learning ◽  
Vector Space ◽  
Real Time ◽  
Learning Model ◽  
Input Vector ◽  
Ship Motions ◽  
Time Prediction ◽  
Deep Learning Model
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