A scoping review and quality assessment of machine learning techniques in identifying maternal risk factors during the peripartum phase for adverse child development

Hsing-Fen Tu, Larissa Zierow, Mattias Lennartsson and Sascha Schweitzer

PLOS ONE, 2025, vol. 20, issue 5, 1-36

Abstract: Maternal exposure to environmental risk factors (e.g., heavy metal exposure) or mental health problems during the peripartum phase has been shown to lead to negative and lasting impacts on child development and life in adulthood. Given the importance of identifying early markers within highly complex and heterogeneous perinatal factors, machine learning techniques emerge as a promising tool. The main goal of the current scoping review was to summarize the evidence on the application of machine learning techniques in predicting or identifying risk factors during peripartum for child development. A critical appraisal was also conducted to evaluate various aspects, including representativeness, data leakage, validation, performance metrics, and interpretability. A systematic search was conducted in PubMed, Web of Science, Scopus, and Google Scholar to identify studies published prior to the 14th of January 2025. Review selection and data extraction were performed by three independent reviewers. After removing duplicates, the searches yielded 10,336 studies, of which 60 studies were included in the final report. Among these 60 machine learning studies, a majority were pattern-focused, using machine learning primarily as a tool to more accurately describe associations between variables, while 16 studies were prediction-focused (26.7%), exploring the predictive performance of their models. For prediction-focused machine learning studies, a diverse range of methodologies was observed. The quality assessment showed that all studies had some important criteria that were not fully met, with deviations ranging from minor to major, limiting the interpretability and generalizability of the reported findings. Future research should aim at addressing these limitations to enhance the robustness and applicability of machine learning models in this field.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0321268

DOI: 10.1371/journal.pone.0321268

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