An innovative supervised longitudinal learning procedure of recurrent neural networks with temporal data augmentation: Insights from predicting fetal macrosomia and large-for-gestational age.

Publisher: Elsevier Country of Publication: United States NLM ID: 1250250 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-0534 (Electronic) Linking ISSN: 00104825 NLM ISO Abbreviation: Comput Biol Med Subsets: MEDLINE

Publication: New York : Elsevier
Original Publication: New York, Pergamon Press.

Fetal Macrosomia*/diagnostic imaging ; Neural Networks, Computer*; Humans ; Female ; Pregnancy ; Infant, Newborn ; Birth Weight ; Gestational Age ; Adult ; Supervised Machine Learning ; Ultrasonography, Prenatal/methods

Background: Longitudinal data in health informatics studies often present challenges due to sparse observations from each subject, limiting the application of contemporary deep learning for prediction. This issue is particularly relevant in predicting birthweight, a crucial factor in identifying conditions such as macrosomia and large-for-gestational age (LGA). Previous approaches have relied on empirical formulas for estimated fetal weights (EFWs) from ultrasound measurements and mixed-effects models for interim predictions.
Method: The proposed novel supervised longitudinal learning procedure features a three-step approach. First, EFWs are generated using empirical formulas from ultrasound measurements. Second, nonlinear mixed-effects models are applied to create augmented sequences of EFWs, spanning daily gestational timepoints. This augmentation transforms sparse longitudinal data into a dense parallel sequence suitable for training recurrent neural networks (RNNs). A tailored RNN architecture is then devised to incorporate the augmented sequential EFWs along with non-sequential maternal characteristics.
Results: The RNNs are trained on augmented data to predict birthweights, which are further classified for macrosomia and LGA. Application of this supervised longitudinal learning procedure to the Successive Small-for-Gestational-Age Births study yields improved performance in classification metrics. Specifically, sensitivity, area under the receiver operation characteristic curve, and Youden's Index demonstrate enhanced results, indicating the effectiveness of the proposed approach in overcoming sparsity challenges in longitudinal health informatics data.
Conclusions: The integration of mixed-effects models for temporal data augmentation and RNNs on augmented sequences shows effective in accurately predicting birthweights, particularly in the context of identifying excessive fetal growth conditions.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier Ltd. All rights reserved.)

Keywords: Data augmentation; Fetal weight prediction; Large-for-Gestational-Age; Macrosomia; Nonlinear mixed-effects models; Recurrent neural networks

Date Created: 20240531 Date Completed: 20240611 Latest Revision: 20240611

20240612

10.1016/j.compbiomed.2024.108665

38820775