지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2022.7
- 수록면
- 640 - 647 (8page)
- DOI
- 10.3349/ymj.2022.63.7.640
이용수
초록· 키워드
오류제보하기
Purpose: The aims of the study were to develop and evaluate a machine learning model with which to predict postnatal growthfailure (PGF) among very low birth weight (VLBW) infants.
Materials and Methods: Of 10425 VLBW infants registered in the Korean Neonatal Network between 2013 and 2017, 7954 infantswere included. PGF was defined as a decrease in Z score >1.28 at discharge, compared to that at birth. Six metrics [area under the re ceiver operating characteristic curve (AUROC), accuracy, precision, sensitivity, specificity, and F1 score] were obtained at five timepoints (at birth, 7 days, 14 days, 28 days after birth, and at discharge). Machine learning models were built using four different tech niques [extreme gradient boosting (XGB), random forest, support vector machine, and convolutional neural network] to compareagainst the conventional multiple logistic regression (MLR) model.
Results: The XGB algorithm showed the best performance with all six metrics across the board. When compared with MLR, XGBshowed a significantly higher AUROC (p=0.03) for Day 7, which was the primary performance metric. Using optimal cut-off points,for Day 7, XGB still showed better performances in terms of AUROC (0.74), accuracy (0.68), and F1 score (0.67). AUROC valuesseemed to increase slightly from birth to 7 days after birth with significance, almost reaching a plateau after 7 days after birth.
Conclusion: We have shown the possibility of predicting PGF through machine learning algorithms, especially XGB. Such mod els may help neonatologists in the early diagnosis of high-risk infants for PGF for early intervention.
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