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.

Prediction In-Hospital Cardiac Arrest within 8 hours using Vital signs

Title : Prediction In-Hospital Cardiac Arrest within 8 hours using Vital signs
Published in : The 16th Asia Pacific International Conference
on Information Science and Technology (APIC-IST 2021)
Author : Minsu Chae, Sangwook Han, Hyo-Wook Gil, Jun Ma, Min Hong, HwaMin Lee
Corresponding author : HwaMin Lee
Location : Paradise Hotel Busan, Korea

Abstract
The rapid response system in the hospital uses the early warning score (EWS) to predict in-hospital cardiac arrest. However, the traditional EWS has low precision and low recall. Since the precision is less than 4%, there has a problem with false alarms. We performed a retrospective cohort study in Soonchunhyang University Cheonan Hospital, which is a tertiary teaching hospital in the Republic of Korea. We performed by changing the data slice size to 8, 16, 24, 32, 40, 48, 56 hours. The deep learning model implemented in this paper has higher precision and recall than the traditional EWS.

Acknowledgments
This research was supported by Basic Science Research Program and the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT) (NRF-2021R1A2C1009290 & No. NRF-2019M3E5D1A02069073). This research was supported by X-mind Corps program of National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT (No. 2019H1D8A1105622)

머신러닝 기반의 약물 중독에 의한 호흡부전 예측

제목 : 머신러닝 기반의 약물 중독에 의한 호흡부전 예측
저자 : 한상욱, 채민수, 이화민
게재지 : 2021년 한국인터넷정보학회 춘계학술발표대회
장소 : 경주, 코모도호텔 경주
교신저자 : 이화민

초록
농촌지역 약물 중독으로 인한 사망자 수는 2020년 855명 이상으로 자궁암, 간염, 뇌종양, 식도암, 백혈병, 유방암, 알츠하이머 등의 질병으로 사망한 사람들의 숫자와 비교하여 결코 적지 않다. 우리나라 농업인의 급성 농약중독 유병률은 22.9~86.7%로 다양하다. 지역, 대상, 계절, 조사방법 등에 따라 상당한 차이가 있다. 농촌진흥청에서 정확한 피해 실태와 함께 그 전모를 파악하기 어려우며, 적절한 대책을 세울 수 없다고 설명하고 있다. 따라서 오직 예방 대책만이 강조되는 수준에 있다. 본 논문에서는 생체신호와 임상검사의 시계열 의료 데이터를 이용하여 약물 중독 환자의 호흡부전 증상을 예측하였다. 순천향대학교 천안병원에서 제공받은 1548명의 환자데이터를 기반으로 연구를 진행 하였으며, 생체정보와 Lab data를 합친 입력 요소 91개를 활용하였다.

Acknowledgments
이 논문은 2021년도 정부(과학기술정보통신부)의 재원으로 한국연구재단의 중견연구지원사업과 현장맞춤형 이공계 인재양성지원사업의 지원을 받아 수행된 연구임. (NRF-2021R1A2C1009290 & No. 2019H1D8A1105622).

생징후 데이터를 이용한 8시간 이내 심정지 예측

제목 : 생징후 데이터를 이용한 8시간 이내 심정지 예측
저자 : 채민수, 한상욱, 한밀라, 홍민, 이화민
게재지 : 2021년 한국인터넷정보학회 춘계학술발표대회
장소 : 경주, 코모도호텔 경주
교신저자 : 이화민

초록
심정지는 심장이 멈추어 혈액이 흐르지 않는 현상이다. 혈액이 흐르지 않을 경우 신체 기능이 손상되며, 뇌손상 혹은 사망에 이를 수 있다. 그렇기 때문에 심정지 조기발견이 매우 중요하다. 심정지 조기 대응의 골든타임은 3분 이내 이다. 병원에서 신속대응팀에서 MEWS는 낮은 재현율과 높은 거짓알람의 문제점이 존재한다. 우리는 이러한 문제를 해결하기 위해 생징후 데이터를 사용하여 딥러닝 기반으로 8시간 이내 심정지를 예측하였다. 시퀀스 길이를 8시간, 16시간, 24시간, 32시간, 40시간, 48시간, 56시간, 64시간으로 나누어 실험하였다. 그 결과 시퀀스 길이가 56시간일 때 정밀도와 재현율이 가장 높았다. 시퀀스 길이를 56으로 고정하고 SMOTE 비율을 변경하여 0.1부터 1.0 까지 실험을 하였다. 그 결과 정밀도가 가장 높은 것은 SMOTE 비율을 0.04로 설정한 것이다. 시퀀스 길이가 56시간이고 SMOTE 비율이 0.04일 때 정밀도는 0.233, 재현율은 0.513, F1 Score는 0.301이다.

Acknowledgments
이 논문은 2021년도 정부(과학기술정보통신부)의 재원으로 한국연구재단의 중견연구지원사업과 바이오·의료기술개발사업의 지원을 받아 수행된 연구임. (NRF-2021R1A2C1009290 & No. NRF-2019M3E5D1A02069073).

Design of Middleware to Support Auto-scaling in Docker-Based Multi Host Environment

Title : Design of Middleware to Support Auto-scaling in Docker-Based Multi Host Environment
Journal : Lecture Notes in Electrical Engineering book
Authors : Minsu Chae, Sangwook Han, HwaMin Lee
Corresponding author : HwaMin Lee
DOI : https://doi.org/10.1007/978-981-15-9343-7_42

Abstract
With the spread of smart devices, the use of big data, and the proliferation of the Internet of Things, virtualization technology for cloud servers have become important worldwide. Also, research has been conducted to efficiently manage the resources of hosts in VMs. Container-based virtualization has less performance degradation than VMs because there is no emulation for the operating system. Using the Docker API is slow to measure. In this paper, we implement the resource measurement module of Job nodes and design middleware that supports auto-scaling in auto-scaling module and Docker-based multi-host environment.

Acknowledgments
This research supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2019-2014-1-00720 & IITP-2019-2015-0-00403) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation).

Deep Learning based Cardiac Arrest Prediction using Vital sign and Lab code

Title : Deep Learning based Cardiac Arrest Prediction using Vital sign and Lab code
Published in : The 12th International Conference on Internet (ICONI 2020)
Author : Minsu Chae, Sangwook Han, Geonil Yun, Hyo-Wook Gil, Min Hong, DoKyeong Lee, HwaMin Lee
Corresponding author : HwaMin Lee
Location : Jeju ShinhwaWorld, Jeju, Korea

Abstract
There are patients in hospitals who are hospitalized for a variety of reasons. We conducted a study on predicting cardiac arrest on patients at Soonchunhyang University Cheonan Hospital. We studied patients from 2016 to 2019. We used deep learning via the LSTM model and the GRU model. We check density of each input feature according to cardiac arrest. We compared only patients with vital signs and lab data. We removed DBP, SBP, BodyTemperature, AST, ALT, WBC, Creatinine, and Bilirubin variable density because there was little difference between those with cardiac arrest and other patients. We experimented with the LSTM model and GRU Model. In this paper, deep learning-based cardiac arrest prediction using vital signs and lab data has high precision. In particular, when using the GRU model, even if the cardiac arrest of 0 to 24 hours for each record is changed, it has a sensitivity of 60%.

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-2019M3E5D1A02069073) and 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)

스마트 시티를 위한 Docker 기반의 모바일 엣지 컴퓨팅 프레임워크 설계

제목 : 스마트 시티를 위한 Docker 기반의 모바일 엣지 컴퓨팅 프레임워크 설계
저자 : 채민수, 한상욱, 윤건일, 조유진, 김영민, 송찬영, 이화민
게재지 : 2020년 한국인터넷정보학회 추계학술발표대회 논문집
장소 : 여수, 여수엑스포컨벤션센터
교신저자 : 이화민

초록
스마트 신호등과 같은 다양한 IoT 기기들로부터 대량의 데이터를 실시간으로 수집하는 경우 트래픽 부하와 집중된 트래픽으로 인한 레이턴시 저하가 발생한다. 그러나 기존 코어 클라우드의 경우 클라이언트에서 클라우드 서버가 있는 IDC까지의 물리적인 레이턴시 한계가 존재한다. 레이턴시를 최소화하기 위하여 Docker 기반의 모바일 엣지 컴퓨팅 프레임워크를 설계하였다.

Acknowledgments
본 연구는 과학기술정보통신부 및 정보통신기획평가원의 대학ICT연구센터육성지원사업의 연구결과로 수행되었음 (IITP-2020-2015-0-00403)
이 논문은 2019년도 정부(과학기술정보통신부)의 재원으로 한국연구재단 -현장맞춤형 이공계 인재양성지원사업의 지원을 받아 수행된 연구임(No. 2019H1D8A1105622).

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.

CNN and LSTM models for predicting particulate matter in the Republic of Korea

Title : CNN and LSTM models for predicting particulate matter in the Republic of Korea
Published in : The 8th International Conference on Information, System and Convergence Applications International Symposium on Innovation in Information Technology and Application 2020 (ICISCA 2020)
Author : Minsu Chae, Sangwook Han, HwaMin Lee
Corresponding author : HwaMin Lee
Location : Ton Duc Thang University, Ho Chi Minh, Vietnam and Online

Abstract
Recently, fine dust has become a problem all over the world. Many countries set regulations for PM10 and PM2.5. Because fine dust adversely affects human health, it is important to alert PM10 and PM2.5 in each city. Predictions for PM10 and PM2.5 are required to alert. We propose a hybrid model of the CNN(Convolutional Neural Network) model and the LSTM(Long Short-Term Memory) model to predict the PM10 and PM2.5 in the Republic of Korea. In addition, we evaluate the performance of the CNN model, the LSTM model, and the proposed hybrid model. As a result of the performance evaluation, the proposed hybrid model has high accuracy.

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 & Future Planning (NRF-2017R1A2B4010570).

Design of middleware to support auto-scaling in Docker-based multi host environment

Title : Design of middleware to support auto-scaling in Docker-based multi host environment
Published in : The 14th KIPS International Conference on Ubiquitous Information Technologies and Applications (CUTE 2019)
Author : Minsu Chae, Sangwook Han, HwaMin Lee
Corresponding author : HwaMin Lee
Location : University of Macau, Macau, China

Abstract
With the spread of smart devices, the use of big data, and the proliferation of the Internet of Things, virtualization technology for cloud servers have become important worldwide. Also, research has been conducted to efficiently manage the resources of hosts in VMs. Container-based virtualization has less performance degradation than VMs because there is no emulation for the operating system. Using the Docker API is slow to measure. In this paper, we implement the resource measurement module of Job nodes and design middleware that supports auto-scaling in auto-scaling module and Docker-based multi-host environment.

Acknowledgments
This research supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2019-2014-1-00720 & IITP-2019-2015-0-00403) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation).