|
|
|
|
이지윤 ( Lee Jee-Yun ) - 고려대학교 의과대학 산부인과학교실
조수정 ( Jo Soo-Jung ) - 고려대학교 의과대학 산부인과학교실
정은진 ( Jung Eun-Jin ) - 고려대학교 의과대학 산부인과학교실
이광식 ( Lee Kwang-Sig ) - 고려대학교 안암병원 AI센터
김승우 ( Kim Seung-Woo ) - 리스크솔루션 인공지능 연구팀
김호연 ( Kim Ho-Yeon ) - 고려대학교 의과대학 산부인과학교실
조금준 ( Cho Geum-Joon ) - 고려대학교 의과대학 산부인과학교실
홍순철 ( Hong Soon-Cheol ) - 고려대학교 의과대학 산부인과학교실
오민정 ( Oh Min-Jeong ) - 고려대학교 의과대학 산부인과학교실
김해중 ( Kim Hai-Joong ) - 고려대학교 의과대학 산부인과학교실
안기훈 ( Ahn Ki-Hoon ) - 고려대학교 의과대학 산부인과학교실
|
|
Abstract
|
|
|
|
|
Purpose: The objective of the present study was to predict the gestational age at preterm birth using artificial neural networks for singleton pregnancy.
Methods: Artificial neural networks (ANNs) were used as a tool for the prediction of gestational age at birth. ANNs trained using obstetrical data of 125 cases, including 56 preterm and 69 non-preterm deliveries. Using a 36-variable obstetrical input set, gestational weeks at delivery were predicted by 89 cases of training sets, 18 cases of validating sets, and 18 cases of testing sets (total: 125 cases). After training, we validated the model by another 12 cases containing data of preterm deliveries.
Results: To define the accuracy of the developed model, we confirmed the correlation coefficient (R) and mean square error of the model. For validating sets, the correlation coefficient was 0.839, but R of testing sets was 0.892, and R of total 125 cases was 0.959. The neural networks were well trained, and the model predictions were relatively good. Furthermore, the model was validated with another dataset of 12 cases, and the correlation coefficient was 0.709. The error days were 11.58±13.73.
Conclusion: In the present study, we trained the ANNs and developed the predictive model for gestational age at delivery. Although the prediction for gestational age at birth in singleton preterm birth was feasible, further studies with larger data, including detailed risk variables of preterm birth and other obstetrical outcomes, are needed.
|
|
|
KeyWords
|
|
preterm delivery, artificial neural networks
|
|
|
원문 및 링크아웃 정보
|
|
|
|
|
등재저널 정보
|
|
|
|
|
|
|