Analysis of factors influencing emergency physician’s choice of specialty again using machine learning method

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

Jee Young Park¹

, Hyung Min Lee¹

, Dong Hoon Key¹⁰

, Beom Sok Seo¹¹

, Suk Jae Choi¹⁴

, 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.

ABSTRACT

Objective:
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.
Method:
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.
Results:
‘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.
Conclusion:
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