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J Korean Soc Emerg Med > Volume 33(4 Suppl.); 2022 > Article
Journal of The Korean Society of Emergency Medicine 2022;33(4 Suppl.): 57-66.
머신러닝 기법을 활용한 응급의학 전문의들의 재선택에 영향을 미치는 요인 분석
박지영1 , 이형민1 , 조광현2 , 김인병3 , 이미진4 , 윤유상5 , 박경혜6,7 , 박송이8 , 김홍재9 , 기동훈10 , 서범석11 , 주영민12 , 지창근13 , 최석재14 , 여인환4 , 강지훈5 , 정우진7 , 임대성15 , 이의선16
1경희대학병원 응급의학과
2을지대학교 노원을지병원 응급의학과
3명지병원 응급의학과
4경북대학교 의과대학 응급의학교실
5인제대학교 부산백병원 응급의학과
6연세대학교 원주의과대학 의학교육학과
7연세대학교 원주세브란스 기독병원 응급의학과
8동아대학교 의과대학 응급의학교실
9KS병원 응급의학과
10가톨릭대학교 여의도성모병원 응급의학과
11순천향대학교 서울병원 응급의학과
12고려대학교 구로병원 응급의학과
13여수전남병원 응급의학과
14화홍병원 응급의학과
15창원경상대학교병원 응급의학과
16울산대학교 의과대학 예방의학과
Analysis of factors influencing emergency physician’s choice of specialty again using machine learning method
Jee Young Park1 , Hyung Min Lee1 , Kwang Hyun Cho2 , In Byung Kim3 , Mi Jin Lee4 , Yoo Sang Yoon5 , Kyung Hye Park6,7 , Song Yi Park8 , Hong Jae Kim9 , Dong Hoon Key10 , Beom Sok Seo11 , Young Min Joo12 , Chang Gun Jee13 , Suk Jae Choi14 , In Hwan Yeo4 , Ji Hun Kang5 , Woo Jin Jung7 , Dae Sung Lim15 , Eu Sun Lee16
1Department of Emergency Medicine, Kyung Hee University Hospital, Seoul, Korea
2Department of Emergency Medicine, Nowon Eulji Medical Center, Eulji University School of Medicine, Seoul, Korea
3Department of Emergency Medicine, Myongji Hospital, Goyang, Korea
4Department of Emergency Medicine, School of Medicine, Kyungpook National University, Daegu, Korea
5Department of Emergency Medicine, Inje University College of Medicine, Busan, Korea
6Department of Medical Education, Yonsei University Wonju College of Medicine, Wonju, Korea
7Department of Emergency Medicine, Wonju Severance Christian Hospital, Wouju, Korea
8Department of Emergency Medicine, Dong-A University College of Medicine, Busan, Korea
9Department of Emergency Medicine, KS Hospital, Gwangju, Korea
10Department of Emergency Medicine, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
11Department of Emergency Medicine, Soonchunhyang University Seoul Hospital, Seoul, Korea
12Department of Emergency Medicine, Korea University Guro Hospital, Seoul, Korea
13Department of Emergency Medicine, Yeosu Jeonnam Hospital, Yeosu, Korea
14Department of Emergency Medicine, Hwahong Hospital, Suwon, Korea
15Department of Emergency Medicine, Gyeongsang National University Changwon Hospital, Changwon, Korea
16Department of Preventive Medicine, University of Ulsan College of Medicine, Korea
Correspondence  Hyung Min Lee ,Tel: 02-958-8585, Fax: 02-958-9689, Email: nice008@naver.com,
Received: June 5, 2021; Revised: September 16, 2021   Accepted: September 23, 2021.  Published online: August 31, 2022.
Machine learning is emerging as a new alternative in various scientific fields and is potentially a new method of interpretation. Using the Light Gradient Boosting Machine (LightGBM), we analyzed the factors that influence the rechoice of emergency medicine responders. The survey is a cross-sectional study which provides an accurate understanding of a responder's current status. However, the results may vary depending on the composition, format, and question, and the relationship between the answers may be unclear.
This study evaluated the modified 2020 Korean Emergency Physician Survey raw data. We applied the preferred model for random relationship check, random forest, support vector machine, and LightGBM models. The stacking ensemble model was used for the final decision process.
‘It is fun working in an emergency room’was the most selected response factor for re-choice, followed by ‘interesting major’. The physical burden of age and lack of identity had a negative impact, whereas burnout and emotional stress factors had a lesser effect. Anxiety caused by the coronavirus disease 2019 (COVID-19) is thought to have a significant impact on this decision making.
Establishing the identity of emergency medicine and being faithful to its fundamental mission is a way to increase the rate of re-choice. Decreasing the burden of workload modified according to age is recommended to establish career longevity. The method of machine learning presents us with a new possibility of checking the relevance of survey results quickly and easily.
Key words: Machine learning; Survey; Emergency medicine; Medical specialty
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