머신러닝을 활용한 응급실 내원 환자 퇴실 조치 결정 조기 예측

다국어 초록 (Multilingual Abstract)

Overcrowding within emergency departments (ED) affects patient satisfaction and quality of care. One of the leading causes of ED overcrowding is the boarding of hospitalized patients in the ED as they await bed placement. Early prediction of disposition of patients can improve patient flow and optimize allocation of hospital resources and bed. Prediction of disposition using supervised machine learning methods are being actively researched abroad. However, there is a need for research suitable for the emergency medical environment in Korea. Previous studies were generally limited to predictions for disposition of either hospital admission or discharge. In this study we attempted to predict disposition (Discharge, General ward admission, ICU admission, Transfer) of patients using initial information of ED patients from the Korean National Emergency Department Information System (NEDIS). We used light gradient boosted machines, Catboost and TabNet. The results showed that TabNet yielded the best performance. This result can aid in decision making by providing standard indicators for hospital admission.

목차 (Table of Contents)

• Abstract
• 1. 서론
• 2. 실험 방법
• 3. 결과
• 4. 결론
• 5. 참고문헌