욕창이 발생하면 환자의 통증과 스트레스가 증가할 뿐만 아니라 치료 기간의 연장과 치료비의 가중으로 인해 신체적, 정신적, 경제적 어려움을 초래하므로 조기진단 및 예측을 통한 욕창의 예방이 매우 중요하다. 본 연구에서는 전자의무기록 중 혈액 검사 데이터에 대한 시계열 분석을 통해 미래의 욕창 발생여부를 예측할 수 있는 딥러닝 기반의 예측 모델을 제안한다. 신뢰도 높은 욕창 발생 여부 예측을 위해 본 연구에서는 욕창 예측에 영향력이 큰 검사 항목을 강조할 수 있도록 새롭게 설계된 attention mechanism과 전자의무기록에 대한 시계열 모델링이 가능한 GRU를 결합한 예측 모델을 제안한다. 제안된 모델의 예측 성능은 accuracy 기준 0.903으로 비교 모델들 대비 우수한 결과를 나타냈다. 본 연구는 전자의무기록 중 비교적 손쉽게 주기적으로 수집될 수 있는 혈액 검사 데이터에 대한 시계열 분석을 통해 정확한 욕창 발생 예측이 가능함을 보였다는 점에서 의의가 있다.
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