Prediction of In-Hospital Cardiac Arrest Using Shallow and Deep Learning

Title : Prediction of In-Hospital Cardiac Arrest Using Shallow and Deep Learning
Journal : Diagnostics
Authors : Minsu Chae, Sangwook Han, Hyowook Gil, Namjun Cho, Hwamin Lee
Corresponding author : Hwamin Lee
DOI : https://doi.org/10.3390/diagnostics11071255

Abstract
Sudden cardiac arrest can leave serious brain damage or lead to death, so it is very important to predict before a cardiac arrest occurs. However, early warning score systems including the National Early Warning Score, are associated with low sensitivity and false positives. We applied shallow and deep learning to predict cardiac arrest to overcome these limitations. We evaluated the performance of the Synthetic Minority Oversampling Technique Ratio. We evaluated the performance using a Decision Tree, a Random Forest, Logistic Regression, Long Short-Term Memory model, Gated Recurrent Unit model, and LSTM–GRU hybrid models. Our proposed Logistic Regression demonstrated a higher positive predictive value and sensitivity than traditional early warning systems.

Acknowledgments
This research was supported by the Bio and Medical Technology Development Program and Basic Science Research Program of the National Research Foundation (NRF) funded by the Korean government (MSIT) (No. NRF-2019M3E5D1A02069073 & NRF-2021R1A2C1009290) and Soonchunhyang University Research Fund.

New Model for Predicting the Presence of Coronary Artery Calcification

Title : New Model for Predicting the Presence of Coronary Artery Calcification
Journal : Journal of Clinical Medicine
Authors : Samel Park, Min Hong, HwaMin Lee, Nam-jun Cho, Eun-Young Lee, Won-Young Lee, Eun-Jung Rhee, Hyo-Wook Gil
Corresponding author : Eun-Jung Rhee, Hyo-Wook Gil
DOI : https://doi.org/10.3390/jcm10030457

Abstract
Coronary artery calcification (CAC) is a feature of coronary atherosclerosis and a well-known risk factor for cardiovascular disease (CVD). As the absence of CAC is associated with a lower incidence rate of CVD, measurement of a CAC score is helpful for risk stratification when the risk decision is uncertain. This was a retrospective study with an aim to build a model to predict the presence of CAC (i.e., CAC score = 0 or not) and evaluate the discrimination and calibration power of the model. Our data set was divided into two set (80% for training set and 20% for test set). Ten-fold cross-validation was applied with ten times of interaction in each fold. We built prediction models using logistic regression (LRM), classification and regression tree (CART), conditional inference tree (CIT), and random forest (RF). A total of 3302 patients from two cohorts (Soonchunhyang University Cheonan Hospital and Kangbuk Samsung Health Study) were enrolled. These patients’ ages were between 40 and 75 years. All models showed acceptable accuracies (LRM, 70.71%; CART, 71.32%; CIT, 71.32%; and RF, 71.02%). The decision tree model using CART and CIT showed a reasonable accuracy without complexity. It could be implemented in real-world practice.

Acknowledgments
This research was supported by the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT) (No. NRF-2019K1A3A1A20093097) and the Soonchunhyang University Research Fund.

Impact of Acid-Base Status on Mortality in Patients with Acute Pesticide Poisoning

Title : Impact of Acid-Base Status on Mortality in Patients with Acute Pesticide Poisoning
Journal : toxics
Authors : Hyo-Wook Gil, Min Hong, HwaMin Lee, Nam-Jun Cho, Eun-Young Lee, Samel Park
Corresponding author : Samel Park
DOI : https://doi.org/10.3390/toxics9020022

Abstract
We investigated clinical impacts of various acid-base approaches (physiologic, base excess (BE)-based, and physicochemical) on mortality in patients with acute pesticide intoxication and mutual intercorrelated effects using principal component analysis (PCA). This retrospective study included patients admitted from January 2015 to December 2019 because of pesticide intoxication. We compared parameters assessing the acid-base status between two groups, survivors and non-survivors. Associations between parameters and 30-days mortality were investigated. A total of 797 patients were analyzed. In non-survivors, pH, bicarbonate concentration (HCO3−), total concentration of carbon dioxide (tCO2), BE, and effective strong ion difference (SIDe) were lower and apparent strong ion difference (SIDa), strong ion gap (SIG), total concentration of weak acids, and corrected anion gap (corAG) were higher than in survivors. In the multivariable logistic analysis, BE, corAG, SIDa, and SIDe were associated with mortality. PCA identified four principal components related to mortality. SIDe, HCO3−, tCO2, BE, SIG, and corAG were loaded to principal component 1 (PC1), referred as total buffer bases to receive and handle generated acids. PC1 was an important factor in predicting mortality irrespective of the pesticide category. PC3, loaded mainly with pCO2, suggested respiratory components of the acid-base system. PC3 was associated with 30-days mortality, especially in organophosphate or carbamate poisoning. Our study showed that acid-base abnormalities were associated with mortality in patients with acute pesticide poisoning. We reduced these variables into four PCs, resembling the physicochemical approach, revealed that PCs representing total buffer bases and respiratory components played an important role in acute pesticide poisoning.

Acknowledgments
This research was supported by the Bio and Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT) (No. NRF-2019M3E5D1A02069073) and by the Soonchunhyang University Research Fund.

Outdoor Particulate Matter Correlation Analysis and Prediction Based Deep Learning in the Korea

Title : Outdoor Particulate Matter Correlation Analysis and Prediction Based Deep Learning in the Korea
Journal : Electronics
Authors : Minsu Chae, Sangwook Han, HwaMin Lee
Corresponding author : HwaMin Lee
DOI : https://doi.org/10.3390/electronics9071146

Abstract
Particulate matter (PM) has become a problem worldwide, with many deleterious health effects such as worsened asthma, affected lungs, and various toxin-induced cancers. The International Agency for Research on Cancer (IARC) under the World Health Organization (WHO) has designated PM as a group 1 carcinogen. Although Korea Environment Corporation forecasts the status of outdoor PM four times a day, whichever is higher among PM10 and PM2.5. Korea Environment Corporation forecasts for the stages of PM. It remains difficult to predict the value of PM when going out. We correlate air quality and solar terms, address format, and weather data, and PM in the Korea. We analyzed the correlation between address format, air quality data, and weather data, and PM. We evaluated performance according to the sequence length and batch size and found the best outcome with a sequence length of 7 days, and a batch size of 96. We performed PM prediction using the Long Short-Term Recurrent Unit (LSTM), the Convolutional Neural Network (CNN), and the Gated Recurrent Unit (GRU) models. The CNN model suffered the limitation of only predicting from the training data, not from the test data. The LSTM and GRU models generated similar prediction results. We confirmed that the LSTM model has higher accuracy than the other two models.

Acknowledgments
This research supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2019-2015-0-00403) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation) and by Soonchunhyang Research Fund.

Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models

Title : Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models
Journal : Sustainability
Authors : Thanongsak Xayasouk, HwaMin Lee, Giyeol Lee
These authors contributed equally to this work : Thanongsak Xayasouk, HwaMin Lee
Corresponding author : Giyeol Lee
DOI : https://doi.org/10.3390/su12062570

Abstract
Many countries worldwide have poor air quality due to the emission of particulate matter (i.e., PM10 and PM2.5), which has led to concerns about human health impacts in urban areas. In this study, we developed models to predict fine PM concentrations using long short-term memory (LSTM) and deep autoencoder (DAE) methods, and compared the model results in terms of root mean square error (RMSE). We applied the models to hourly air quality data from 25 stations in Seoul, South Korea, for the period from 1 January 2015, to 31 December 2018. Fine PM concentrations were predicted for the 10 days following this period, at an optimal learning rate of 0.01 for 100 epochs with batch sizes of 32 for LSTM model, and DAEs model performed best with batch size 64. The proposed models effectively predicted fine PM concentrations, with the LSTM model showing slightly better performance. With our forecasting model, it is possible to give reliable fine dust prediction information for the area where the user is located.

Acknowledgments
This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2020-2015-0-00403)supervised by the IITP(Institute for Information &communications Technology Planning &Evaluation) and by Soonchunhyang Research Fund.

A Hybrid Deep Learning Model to Forecast Particulate Matter Concentration Levels in Seoul, South Korea

Title : A Hybrid Deep Learning Model to Forecast Particulate Matter Concentration Levels in Seoul, South Korea
Journal : Atmosphere
Authors : Guang Yang, HwaMin Lee, Giyeol Lee
These authors contributed equally to this work : Guang Yang, HwaMin Lee
Corresponding author : Giyeol Lee
DOI : https://doi.org/10.3390/atmos11040348

Abstract
Both long- and short-term exposure to high concentrations of airborne particulate matter (PM) severely affect human health. Many countries now regulate PM concentrations. Early-warning systems based on PM concentration levels are urgently required to allow countermeasures to reduce harm and loss. Previous studies sought to establish accurate, efficient predictive models. Many machine-learning methods are used for air pollution forecasting. The long short-term memory and gated recurrent unit methods, typical deep-learning methods, reliably predict PM levels with some limitations. In this paper, the authors proposed novel hybrid models to combine the strength of two types of deep learning methods. Moreover, the authors compare hybrid deep-learning methods (convolutional neural network (CNN)—long short-term memory (LSTM) and CNN—gated recurrent unit (GRU)) with several stand-alone methods (LSTM, GRU) in terms of predicting PM concentrations in 39 stations in Seoul. Hourly air pollution data and meteorological data from January 2015 to December 2018 was used for these training models. The results of the experiment confirmed that the proposed prediction model could predict the PM concentrations for the next 7 days. Hybrid models outperformed single models in five areas selected randomly with the lowest root mean square error (RMSE) and mean absolute error (MAE) values for both PM10 and PM2.5. The error rate for PM10 prediction in Gangnam with RMSE is 1.688, and MAE is 1.161. For hybrid models, the CNN–GRU better-predicted PM10 for all stations selected, while the CNN–LSTM model performed better on predicting PM2.5.

Acknowledgments
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (NRF-2017R1A2B4010570) and Soonchunhyang University Research Fund.

A performance comparison of linux containers and virtual machines using Docker and KVM

Title : A performance comparison of linux containers and virtual machines using Docker and KVM
Journal : Cluster Computing
Authors : MinSu Chae, HwaMin Lee, Kiyeol Lee
Corresponding author : HwaMin Lee
DOI : https://doi.org/10.1007/s10586-017-1511-2

Abstract
Virtualization is a foundational element of cloud computing. Since cloud computing is slower than a native system, this study analyzes ways to improve performance. We compared the performance of Docker and Kernel-based virtual machine (KVM). KVM uses full virtualization, including ×86 hardware virtualization extensions. Docker is a solution provided by isolation in userspace instead of creating a virtual machine. The performance of KVM and Docker was compared in three ways. These comparisons show that Docker is faster than KVM.

Acknowledgments
This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government (Ministry of Science and ICT) (No. NRF-2017R1A2B4010570) and the Soonchunhyang University Research Fund.

Energy efficient VM scheduling for big data processing in cloud computing environments

Title : Energy efficient VM scheduling for big data processing in cloud computing environments
Journal : Journal of Ambient Intelligence and Humanized Computing
Authors : SangWook Han, SeDong Min, HwaMin Lee
Corresponding author : HwaMin Lee
DOI : https://doi.org/10.1007/s12652-019-01361-8

Abstract
Recently, the cloud computing platform has come to be widely used to analyze large amounts of data collected in real-time from SNS or IoT sensors. In order to analyze big data, a large number of VMs are created in the cloud server, and that many PMs are needed to handle it. When VMs are allocated to PMs in cloud computing, each VM is allocated by a VM scheduling algorithm. However, existing scheduling algorithms waste substantial PM resources due to the low density of VM. This waste of resources dramatically reduces the energy efficiency of the entire cloud server. Therefore, minimizing idle PMs by increasing the density of VMs allocated to PMs is critical for VM scheduling. In this paper, a VM relocation method is suggested to improve the energy efficiency by increasing the density of VMs using the Knapsack algorithm. In addition, it is possible through the proposed method to achieve efficient VM relocation in a short period by improving the Knapsack algorithm. Therefore, we proposed the effective resource management method of cloud cluster for big data analysis.

Acknowledgments
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (NRF-2017R1A2B4010570) and by the Soonchunhyang University Research Fund.

Development of Cloud Based Air Pollution Information System Using Visualization

Title : Development of Cloud Based Air Pollution Information System Using Visualization
Journal : Computers, Materials & Continua
Authors : SangWook Han, JungYeon Seo, Dae-Young Kim, SeokHoon Kim, HwaMin Lee
Corresponding author : HwaMin Lee
DOI : https://doi.org/10.32604/cmc.2019.06071

Abstract
Air pollution caused by fine dust is a big problem all over the world and fine dust has a fatal impact on human health. But there are too few fine dust measuring stations and the installation cost of fine dust measuring station is very expensive. In this paper, we propose Cloud-based air pollution information system using R. To measure fine dust, we have developed an inexpensive measuring device and studied the technique to accurately measure the concentration of fine dust at the user’s location. And we have developed the smartphone application to provide air pollution information. In our system, we provide collected data based analytical results through effective data modeling. Our system provides information on fine dust value and action tips through the air pollution information application. And it supports visualization on the map using the statistical program R. The user can check the fine dust statistics map and cope with fine dust accordingly.

Acknowledgments
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2017R1A2B4010570) and by Soonchunhyang Research Fund.

A low cost wearable wireless sensing system for paretic hand management after stroke

Title : A low cost wearable wireless sensing system for paretic hand management after stroke
Journal : The Journal of Supercomputing
Authors : Se-Dong Min, Chang-Won Wang, Hwa-Min Lee, Bong-Keun Jung
Corresponding author : Bong-Keun Jung
DOI : https://doi.org/10.1007/s11227-016-1787-7

Abstract
This paper describes the design of a low cost wearable hand exercise device that can assist repetitive wrist and finger exercise for stroke patients. The design of this device was guided by neurobiological principles of motor learning, such as sensory-motor integration, movement repetition, and cognitive interaction. This pilot study tested the efficacy of a wireless sensing system in the device to serve as a facilitator of repetitive hand exercise, which is an essential part of rehabilitation after stroke. The results from healthy young adults showed that the device with a wireless sensing system yielded quantitatively better motor function with the repetitive wrist and finger joint movements.This proof-of-concept study shows potential therapeutic evidence for stroke rehabilitation as well as the potential utility of sensing system for stroke rehabilitation.

Acknowledgments
“This work was supported by the Soonchunhyang University Research Fund and the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the C-ITRC (Convergence Information Technology Research Center) support program (IITP-2015-H8601-15-1009) supervised by the NIPA (National IT Industry Promotion Agency)”.