Machine learning approach to investigate pregnancy and childbirth risk factors of sleep problems in early adolescence: Evidence from two cohort studies.

Autor: Dai Y; School of Nursing and School of Medicine, University of Pennsylvania, 418 Curie Blvd., Room 426, Claire M. Fagin Hall, Philadelphia, PA 19104-6096, USA., Buttenheim AM; School of Nursing and School of Medicine, University of Pennsylvania, 418 Curie Blvd., Room 426, Claire M. Fagin Hall, Philadelphia, PA 19104-6096, USA., Pinto-Martin JA; School of Nursing and School of Medicine, University of Pennsylvania, 418 Curie Blvd., Room 426, Claire M. Fagin Hall, Philadelphia, PA 19104-6096, USA; Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA., Compton P; School of Nursing and School of Medicine, University of Pennsylvania, 418 Curie Blvd., Room 426, Claire M. Fagin Hall, Philadelphia, PA 19104-6096, USA., Jacoby SF; School of Nursing and School of Medicine, University of Pennsylvania, 418 Curie Blvd., Room 426, Claire M. Fagin Hall, Philadelphia, PA 19104-6096, USA., Liu J; School of Nursing and School of Medicine, University of Pennsylvania, 418 Curie Blvd., Room 426, Claire M. Fagin Hall, Philadelphia, PA 19104-6096, USA. Electronic address: jhliu@nursing.upenn.edu.
Jazyk: angličtina
Zdroj: Computer methods and programs in biomedicine [Comput Methods Programs Biomed] 2024 Nov; Vol. 256, pp. 108402. Date of Electronic Publication: 2024 Aug 28.
DOI: 10.1016/j.cmpb.2024.108402
Abstrakt: Background: This study aimed to predict early adolescent sleep problems using pregnancy and childbirth risk factors through machine learning algorithms, and to evaluate model performance internally and externally.
Methods: Data from the China Jintan Child Cohort study (CJCC; n=848) for model development and the US Healthy Brain and Behavior Study (HBBS; n=454) for external validation were employed. Maternal pregnancy histories, obstetric data, and adolescent sleep problems were collected. Several machine learning techniques were employed, including least absolute shrinkage and selection operator, logistic regression, random forest, naïve bayes, extreme gradient boosting, decision tree, and neural network. The area under the receiver operating characteristic curve, sensitivity, specificity, accuracy, and root mean square of residuals were used to evaluate model performance.
Results: Key predictors for CJCC adolescents' sleep problems include gestational age, birthweight, duration of delivery, and maternal happiness during pregnancy. In HBBS adolescents, the duration of postnatal depressive emotions was the primary perinatal predictor. The prediction models developed in the CJCC had good-to-excellent internal validation performance but poor performance in predicting the sleep problems in HBBS adolescents.
Conclusion: The identification of specific perinatal risk factors associated with adolescent sleep problems can inform targeted interventions during and after pregnancy to mitigate these risks. Health providers should consider integrating these predictive factors into routine pre- and postnatal assessments to identify at-risk populations. The variability in model performance across different cohorts highlights the need for context-specific models and the cautious application of predictive analytics across diverse populations. Future research should focus on refining predictive models to account for such variations, potentially through the incorporation of additional socio-cultural factors and genetic markers. This study emphasizes the importance of personalized and culturally sensitive approaches in the prediction and management of adolescent sleep problems, leveraging advanced computational methods to enhance maternal and child health outcomes.
Competing Interests: Declaration of competing interest This work has not been published previously, and it is not under consideration for publication elsewhere. This submission is approved by all authors, and if accepted, it will not be published elsewhere in the same form in English or in any other language including electronically without the written consent of the copyright-holder.
(Copyright © 2024 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
Externí odkaz: