scholarly journals Artificial Intelligence Algorithm for Predicting Cardiac Arrest Using Electrocardiography

2020 ◽  
Author(s):  
Joon-myoung Kwon ◽  
Kyung-Hee Kim ◽  
Ki-Hyun Jeon ◽  
Soo Youn Lee ◽  
Jinsik Park ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Intensive Care Unit ◽  
Cardiac Arrest ◽  
Intensive Care ◽  
External Validation ◽  
High Risk Group ◽  
Validation Data ◽  
Intelligence Algorithm ◽  
Artificial Intelligence Algorithm ◽  
Intensive Care Unit Transfer

Abstract Background: In-hospital cardiac arrest is a major burden in health care. Although several track-and-trigger systems are used to predict cardiac arrest, they often have unsatisfactory performances. We hypothesized that a deep-learning-based artificial intelligence algorithm (DLA) could effectively predict cardiac arrest using electrocardiography (ECG). We developed and validated a DLA for predicting cardiac arrest using ECG. Methods: We conducted a retrospective study that included 47,505 ECGs of 25,672 adult patients admitted to two hospitals, who underwent at least one ECG from October 2016 to September 2019. The endpoint was occurrence of cardiac arrest within 24 hours from ECG. Using subgroup analyses in patients who were initially classified as non-event, we confirmed the delayed occurrence of cardiac arrest and unexpected intensive care unit transfer over 14 days.Results: We used 32,294 ECGs of 10,461 patients and 4,483 ECGs of 4,483 patients from a hospital were used as development and internal validation data, respectively. Additionally, 10,728 ECGs of 10,728 patients from another hospital were used as external validation data, which confirmed the robustness of the developed DLA. During internal and external validation, the areas under the receiver operating characteristic curves of the DLA in predicting cardiac arrest within 24 hours were 0.913 and 0.948, respectively. The high risk group of the DLA showed a significantly higher hazard for delayed cardiac arrest (5.74% vs. 0.33%, P < 0.001) and unexpected intensive care unit transfer (4.23% vs. 0.82%, P < 0.001). A sensitivity map of the DLA displayed the ECG regions used to predict cardiac arrest, with the DLA focused most on the QRS complex. Conclusions: Our DLA successfully predicted cardiac arrest using diverse formats of ECG. The results indicate that cardiac arrest could be screened and predicted not only with a conventional 12-lead ECG, but also with a single-lead ECG using a wearable device that employs our DLA.

scholarly journals Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography

2020 ◽  
Author(s):  
Joon-myoung Kwon ◽  
Kyung-Hee Kim ◽  
Ki-Hyun Jeon ◽  
Soo Youn Lee ◽  
Jinsik Park ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Intensive Care Unit ◽  
Cardiac Arrest ◽  
Intensive Care ◽  
External Validation ◽  
High Risk Group ◽  
Validation Data ◽  
Intelligence Algorithm ◽  
Artificial Intelligence Algorithm ◽  
Intensive Care Unit Transfer

Abstract Background: In-hospital cardiac arrest is a major burden in health care. Although several track-and-trigger systems are used to predict cardiac arrest, they often have unsatisfactory performances. We hypothesized that a deep-learning-based artificial intelligence algorithm (DLA) could effectively predict cardiac arrest using electrocardiography (ECG). We developed and validated a DLA for predicting cardiac arrest using ECG. Methods: We conducted a retrospective study that included 47,505 ECGs of 25,672 adult patients admitted to two hospitals, who underwent at least one ECG from October 2016 to September 2019. The endpoint was occurrence of cardiac arrest within 24 hours from ECG. Using subgroup analyses in patients who were initially classified as non-event, we confirmed the delayed occurrence of cardiac arrest and unexpected intensive care unit transfer over 14 days.Results: We used 32,294 ECGs of 10,461 patients and 4,483 ECGs of 4,483 patients from a hospital were used as development and internal validation data, respectively. Additionally, 10,728 ECGs of 10,728 patients from another hospital were used as external validation data, which confirmed the robustness of the developed DLA. During internal and external validation, the areas under the receiver operating characteristic curves of the DLA in predicting cardiac arrest within 24 hours were 0.913 and 0.948, respectively. The high risk group of the DLA showed a significantly higher hazard for delayed cardiac arrest (5.74% vs. 0.33%, P < 0.001) and unexpected intensive care unit transfer (4.23% vs. 0.82%, P < 0.001). A sensitivity map of the DLA displayed the ECG regions used to predict cardiac arrest, with the DLA focused most on the QRS complex. Conclusions: Our DLA successfully predicted cardiac arrest using diverse formats of ECG. The results indicate that cardiac arrest could be screened and predicted not only with a conventional 12-lead ECG, but also with a single-lead ECG using a wearable device that employs our DLA.

scholarly journals Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography

2020 ◽  
Vol 28 (1) ◽  
Author(s):  
Joon-myoung Kwon ◽  
Kyung-Hee Kim ◽  
Ki-Hyun Jeon ◽  
Soo Youn Lee ◽  
Jinsik Park ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Intensive Care Unit ◽  
Cardiac Arrest ◽  
Intensive Care ◽  
External Validation ◽  
High Risk Group ◽  
Validation Data ◽  
Intelligence Algorithm ◽  
Artificial Intelligence Algorithm ◽  
Intensive Care Unit Transfer

Abstract Background In-hospital cardiac arrest is a major burden in health care. Although several track-and-trigger systems are used to predict cardiac arrest, they often have unsatisfactory performances. We hypothesized that a deep-learning-based artificial intelligence algorithm (DLA) could effectively predict cardiac arrest using electrocardiography (ECG). We developed and validated a DLA for predicting cardiac arrest using ECG. Methods We conducted a retrospective study that included 47,505 ECGs of 25,672 adult patients admitted to two hospitals, who underwent at least one ECG from October 2016 to September 2019. The endpoint was occurrence of cardiac arrest within 24 h from ECG. Using subgroup analyses in patients who were initially classified as non-event, we confirmed the delayed occurrence of cardiac arrest and unexpected intensive care unit transfer over 14 days. Results We used 32,294 ECGs of 10,461 patients and 4483 ECGs of 4483 patients from a hospital were used as development and internal validation data, respectively. Additionally, 10,728 ECGs of 10,728 patients from another hospital were used as external validation data, which confirmed the robustness of the developed DLA. During internal and external validation, the areas under the receiver operating characteristic curves of the DLA in predicting cardiac arrest within 24 h were 0.913 and 0.948, respectively. The high risk group of the DLA showed a significantly higher hazard for delayed cardiac arrest (5.74% vs. 0.33%, P < 0.001) and unexpected intensive care unit transfer (4.23% vs. 0.82%, P < 0.001). A sensitivity map of the DLA displayed the ECG regions used to predict cardiac arrest, with the DLA focused most on the QRS complex. Conclusions Our DLA successfully predicted cardiac arrest using diverse formats of ECG. The results indicate that cardiac arrest could be screened and predicted not only with a conventional 12-lead ECG, but also with a single-lead ECG using a wearable device that employs our DLA.

scholarly journals Artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Younghoon Cho ◽  
Joon-myoung Kwon ◽  
Kyung-Hee Kim ◽  
Jose R. Medina-Inojosa ◽  
Ki-Hyun Jeon ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Myocardial Infarction ◽  
Operating Characteristic ◽  
External Validation ◽  
Receiver Operating Characteristic Curves ◽  
Treatment And Prevention ◽  
Intelligence Algorithm ◽  
Variational Autoencoder ◽  
Artificial Intelligence Algorithm ◽  
Life Type

AbstractRapid diagnosis of myocardial infarction (MI) using electrocardiography (ECG) is the cornerstone of effective treatment and prevention of mortality; however, conventional interpretation methods has low reliability for detecting MI and is difficulty to apply to limb 6-lead ECG based life type or wearable devices. We developed and validated a deep learning-based artificial intelligence algorithm (DLA) for detecting MI using 6-lead ECG. A total of 412,461 ECGs were used to develop a variational autoencoder (VAE) that reconstructed precordial 6-lead ECG using limb 6-lead ECG. Data from 9536, 1301, and 1768 ECGs of adult patients who underwent coronary angiography within 24 h from each ECG were used for development, internal and external validation, respectively. During internal and external validation, the area under the receiver operating characteristic curves of the DLA with VAE using a 6-lead ECG were 0.880 and 0.854, respectively, and the performances were preserved by the territory of the coronary lesion. Our DLA successfully detected MI using a 12-lead ECG or a 6-lead ECG. The results indicate that MI could be detected not only with a conventional 12 lead ECG but also with a life type 6-lead ECG device that employs our DLA.

scholarly journals Surviving cardiac arrest: What happens after admission to the intensive care unit?

2021 ◽  
Author(s):  
Raquel Menezes Fernandes ◽  
Daniel Nuñez ◽  
Nuno Marques ◽  
Cláudia Camila Dias ◽  
Cristina Granja
Keyword(s):  
Intensive Care Unit ◽  
Cardiac Arrest ◽  
Intensive Care

scholarly journals Development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bongjin Lee ◽  
Kyunghoon Kim ◽  
Hyejin Hwang ◽  
You Sun Kim ◽  
Eun Hee Chung ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Validation Cohort ◽  
External Validation ◽  
Learning Model ◽  
Derivation Cohort ◽  
Icu Admission ◽  
Early Stages ◽  
Machine Learning Model

AbstractThe aim of this study was to develop a predictive model of pediatric mortality in the early stages of intensive care unit (ICU) admission using machine learning. Patients less than 18 years old who were admitted to ICUs at four tertiary referral hospitals were enrolled. Three hospitals were designated as the derivation cohort for machine learning model development and internal validation, and the other hospital was designated as the validation cohort for external validation. We developed a random forest (RF) model that predicts pediatric mortality within 72 h of ICU admission, evaluated its performance, and compared it with the Pediatric Index of Mortality 3 (PIM 3). The area under the receiver operating characteristic curve (AUROC) of RF model was 0.942 (95% confidence interval [CI] = 0.912–0.972) in the derivation cohort and 0.906 (95% CI = 0.900–0.912) in the validation cohort. In contrast, the AUROC of PIM 3 was 0.892 (95% CI = 0.878–0.906) in the derivation cohort and 0.845 (95% CI = 0.817–0.873) in the validation cohort. The RF model in our study showed improved predictive performance in terms of both internal and external validation and was superior even when compared to PIM 3.

scholarly journals Prediction of velocity profile of water based copper nanofluid in a heated porous tube using CFD and genetic algorithm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tiziana Ciano ◽  
Massimiliano Ferrara ◽  
Meisam Babanezhad ◽  
Afrasyab Khan ◽  
Azam Marjani
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Velocity Profile ◽  
Fluid Velocity ◽  
Accurate Prediction ◽  
Inference System ◽  
Porous Tube ◽  
Intelligence Algorithm ◽  
Water Based ◽  
Artificial Intelligence Algorithm

AbstractThe heat transfer improvements by simultaneous usage of the nanofluids and metallic porous foams are still an attractive research area. The Computational fluid dynamics (CFD) methods are widely used for thermal and hydrodynamic investigations of the nanofluids flow inside the porous media. Almost all studies dedicated to the accurate prediction of the CFD approach. However, there are not sufficient investigations on the CFD approach optimization. The mesh increment in the CFD approach is one of the challenging concepts especially in turbulent flows and complex geometries. This study, for the first time, introduces a type of artificial intelligence algorithm (AIA) as a supplementary tool for helping the CFD. According to the idea of this study, the CFD simulation is done for a case with low mesh density. The artificial intelligence algorithm uses learns the CFD driven data. After the intelligence achievement, the AIA could predict the fluid parameters for the infinite number of nodes or dense mesh without any limitations. So, there is no need to solve the CFD models for further nodes. This study is specifically focused on the genetic algorithm-based fuzzy inference system (GAFIS) to predict the velocity profile of the water-based copper nanofluid turbulent flow in a porous tube. The most intelligent GAFIS could perform the most accurate prediction of the velocity. Hence, the intelligence of GAFIS is tested for different values of cluster influence range (CIR), squash factor(SF), accept ratio (AR) and reject ratio (RR), the population size (PS), and the percentage of crossover (PC). The maximum coefficient of determination (~ 0.97) was related to the PS of 30, the AR of 0.6, the PC of 0.4, CIR of 0.15, the SF 1.15, and the RR of 0.05. The GAFIS prediction of the fluid velocity was in great agreement with the CFD. In the most intelligent condition, the velocity profile predicted by GAFIS was similar to the CFD. The nodes increment from 537 to 7671 was made by the GAFIS. The new predictions of the GAFIS covered all CFD results.

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