Annals of epidemiology · 2025
Transfer learning for COVID-19 predictive modeling: A multicenter study of 12 hospitals.
СтатьяМногоцентровое исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Savalli Carine, Carneiro André Henrique Alves, Filho Fabiano Barcellos, Bigoto Murilo Afonso Robiati, Wichmann Roberta Moreira, Chiavegatto Filho Alexandre Dias Porto, IACOV-BR Network
Аннотация
PURPOSE: To use transfer learning to predict Intensive Care Unit admission (ICU) for COVID-19 patients in a multicenter setting.
METHODS: We trained XGBoost algorithms using demographic and laboratory predictors recorded during hospital admissions across 12 hospitals. Our goal was to identify the best-performing hospital and evaluate its generalization capacity through external validation in the other 11 hospitals. Additionally, this best-performing hospital served as the source domain for transfer learning and was fine-tuned by incorporating new decision trees trained with local data from each of the remaining hospitals.
RESULTS: We observed considerable variation in ICU admission outcomes across the 12 hospitals, with local predictive performance (AUC - area under the receiver operating characteristic curve) ranging from 0.6239 to 0.9410. The best-performing model's generalization capacity, evaluated through external validation, resulted in an AUC below 0.7 in 6 hospitals. By applying transfer learning and fine-tuning this best-performing model, we found that its performance stabilized after incorporating 20 new trees trained with data from the target hospitals. This adjustment improved AUC in 9 out of the 11 hospitals, compared to the results from the external validation.
CONCLUSIONS: The study showed that transfer learning effectively leverages knowledge from the best-performing hospital to initialize and accelerate model training in other hospitals with lower performance or limited sample sizes, thereby enhancing overall adaptability and efficiency.
METHODS: We trained XGBoost algorithms using demographic and laboratory predictors recorded during hospital admissions across 12 hospitals. Our goal was to identify the best-performing hospital and evaluate its generalization capacity through external validation in the other 11 hospitals. Additionally, this best-performing hospital served as the source domain for transfer learning and was fine-tuned by incorporating new decision trees trained with local data from each of the remaining hospitals.
RESULTS: We observed considerable variation in ICU admission outcomes across the 12 hospitals, with local predictive performance (AUC - area under the receiver operating characteristic curve) ranging from 0.6239 to 0.9410. The best-performing model's generalization capacity, evaluated through external validation, resulted in an AUC below 0.7 in 6 hospitals. By applying transfer learning and fine-tuning this best-performing model, we found that its performance stabilized after incorporating 20 new trees trained with data from the target hospitals. This adjustment improved AUC in 9 out of the 11 hospitals, compared to the results from the external validation.
CONCLUSIONS: The study showed that transfer learning effectively leverages knowledge from the best-performing hospital to initialize and accelerate model training in other hospitals with lower performance or limited sample sizes, thereby enhancing overall adaptability and efficiency.