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)

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).

심정지 발생 예측 시스템 및 방법

발명의 명칭 : 심정지 발생 예측 시스템 및 방법
출원번호 : 10-2021-0022359
출원일자 : 2021년 2월 19일
발명자 : 이화민, 길효욱, 조남준, 채민수, 윤건일
특허권자 : 순천향대학교 산학협력단
공개신청 : 무

연구사사
【이 발명을 지원한 국가연구개발사업】
【과제고유번호】 1711104866
【과제번호】 2019M3E5D1A02069073
【부처명】 다부처
【과제관리(전문)기관명】 한국연구재단
【연구사업명】 혁신형의사과학자공동연구사업(R&D)(복지부,
과기정통부)
【연구과제명】 일회용 패치 기반의 환자 모니터링 시스템 개발
【기여율】 1/2
【과제수행기관명】 순천향대학교 산학협력단
【연구기간】 2019.07.01 ~ 2022.12.31

【이 발명을 지원한 국가연구개발사업】
【과제고유번호】 1711093210
【과제번호】 20150004030041001
【부처명】 과학기술정보통신부
【과제관리(전문)기관명】 정보통신기술진흥센터(IITP)
【연구사업명】 대학ICT연구센터육성
【연구과제명】 IoT 보안기술연구
【기여율】 1/2
【과제수행기관명】 순천향대학교 산학협력단
【연구기간】 2015.06.01 ~ 2021.03.31

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.

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)

생징후 데이터를 이용한 딥러닝 기반의 심정지 예측

제목 : 생징후 데이터를 이용한 딥러닝 기반의 심정지 예측
저자 : 채민수, 윤건일, 박찬영, 조유진, 이화민
게재지 : 2020년 한국인터넷정보학회 춘계학술발표대회 논문집
장소 : 제주도, 오션스위츠 제주호텔
교신저자 : 이화민

초록
병원에는 다양한 원인으로 입원하는 환자들이 있다. 심정지의 초기 발견 및 초기대응이 중요하다. 본 논문에서는 순천향대학교 천안병원의 2016년부터 2019년까지의 병동 환자들을 대상으로 심정지 예측에 대한 연구를 진행하였다. 딥 러닝 학습 시 측정시간, 나이, 성별, DBP, SBP, 맥박, 호흡, 체온, 심정지 여부를 1시간 단위로 레코드를 만들어 72개의 레코드 단위로 학습하였다. LSTM 모델을 적용하여 심정지 예측를 수행하였다. 수행결과 정확도는 약 99.9%, MAE는 0.00006517576735498417, RMSE는 0.003397121177224215 이다.

Acknowledgments
이 논문은 과학기술정보통신부의 재원으로 한국연구재단 바이오․의료기술개발사업의 지원을 받아 수행된 연구임(No. NRF-2019M3E5D1A02069073).
본 연구는 순천향대학교 학술연구비 지원으로 수행하였습니다.

A Design of Cardiac Arrest Early Warning System in Hospital

Title : A Design of Cardiac Arrest Early Warning System in Hospital
Published in : The 3rd International Conference on Interdisciplinary Research on Computer Science, Psychology, and Education (ICICPE’ 2019)
Author : SangWook Han, MinSu Chae, Min Hong, HwaMin Lee
Corresponding author : HwaMin Lee
Location : Vinoasis Phu Quoc Hotel, Phu Qoc, Vietnam

Abstract
Recently, increasing heart failure patients due to the increase in elderly patients, high probability for these patients, the arrhythmia. The number of cardiac arrests in hospitals is also increasing, and mortality rates are more than twice as high as those transferred from general wards to the intensive care unit through the emergency room or other routes. Existing patient monitoring methods in hospitals are measured using 24-hour Holter monitoring. This method requires carrying the device and measuring up to 48 hours. Also, the patient’s activities are very uncomfortable because of the communication line. Currently, many hospitals use the Modified Early Warning Score (MEWS) as a standard for early detection of deterioration in general ward patients. However, the prediction rate is very low and there is little help. Many people are working hard to detect heart attacks early because computerized systems vary from hospital to hospital, it is difficult to apply the developed system to various hospital environments. This study discusses the factors and methods necessary to solve these problems.

Acknowledgments
This research was supported by MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2019-2014-1-00720) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation) and the Bio & Medical Technology Development Program of the National Research Foundation (NRF)(No.NRF-2019M3E5D1A02069073).