Seq2Seq 딥러닝 구조를 활용한 임상정보 기반의 COVID-19 확진자 중증도 예측 모델

초록

Background: The COVID-19 pandemic has disrupted healthcare systems worldwide, with overwhelmed facilities leading to high morbidity and mortality rates. Deep learning models that predict patient severity can aid in optimizing resource allocation and patient monitoring. However, conventional models rely on excessive clinical features, reduce generalizability, and fail to provide real-time severity tracking. This study proposes a sequence-to-sequence (Seq2Seq) deep-learning model for predicting COVID-19 severity using minimal clinical features. Methods: Data from 4,462 patients from two tertiary care hospitals in Daegu, Korea (2020– 2022) were used to train the model, with 442 external validation cases collected from the National Institute of Health in Korea. Seq2SeqAttn inputs the observation of 17 clinical features of at most five days and outputs the predicted severity level of up to three days. Results: The model achieved a 98% recall and 97.6% receiver operating characteristic curve for validation. Seq2SeqAttn correctly identified severe cases, with lactate dehydrogenase (LDH) and neutrophil-lymphocyte ratios significantly differing between the severity groups. Integrated gradients revealed that peripheral oxygen saturation and LDH levels were critical predictors. The model outperformed conventional severity assessment tools, such as the WHO Clinical Progression Scale and National Early Warning Score. Conclusion: This study presented a real-time COVID-19 severity prediction model using minimal clinical features. The high accuracy and interpretability of the model demonstrates its potential to improve resource allocation and patient care during pandemics. Future studies should investigate its applicability to other respiratory and infectious diseases.

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