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

Aug 4, 2026

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Maternal exposure to environmental hazards such as heavy metals, and to peripartum mental health problems, can produce negative and durable effects on child development and later life outcomes. The review targets the challenge of detecting early markers within complex, heterogeneous perinatal factors by synthesizing evidence on machine learning applications for identifying or predicting peripartum risk factors. It also critically appraises representativeness, data leakage, validation, performance metrics, and interpretability.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages36

Reading Time1 h 31 min

AccessFree to download

Abstract

Introduction

Definition of the peripartum phase and developmental importance

Rationale for early markers and predictors

Complexity of perinatal influences

Machine learning definitions and study types

OPEN ACCESS Citation: Tu H-F, Zierow L, Lennartsson M, Schweitzer S (2025) A scoping review and quality assessment of machine learning techniques in identifying maternal risk factors during the peripartum phase for adverse child development. PLoS One 20(5): e0321268 . [https://doi.org/10.1371/journal.pone.0321268](https://doi.org/10.1371/journal.pone.0321268)[ ](https://doi.org/10.1371/journal.pone.0321268)[Editor:](Editor: Nhu N. Tran)[ Nhu N. Tran](Editor: Nhu N. Tran), Children's Hospital of Los Angeles / Keck School of Medicine, UNITED STATES OF AMERICA Received: December 11, 2023 Accepted: March 4, 2025 Published: May 28, 2025 Copyright: © 2025 Tu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data availability statement: All relevant files are available from the Open Science Framework database (URL: [https://osf.io/n5gyd/](https://osf.io/n5gyd/)). Funding: This work was supported by a grant from Gillbergska stiftelsen (Grant no. 2022) RESEARCH ARTICLE 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 Tu1,2,3, Larissa Zierow4,5, Mattias Lennartsson6, Sascha Schweitzer4,7* 1 Department of Women’s and Children’s Health, Uppsala University, Uppsala, Sweden, 2 Department of Psychology, Uppsala University, Uppsala, Sweden, 3 Department of Applied Educational Science, Umeå University, Umeå, Sweden, 4 ESB Business School, Reutlingen University, Reutlingen, Germany, 5 ifo Institute, CESifo, Munich, Germany, 6 Umeå University Library, Umeå, Sweden, 7 Faculty of Law and Economics, University of Bayreuth, Bayreuth, Germany * [sascha.schweitzer@reutlingen-university.de](sascha.schweitzer@reutlingen-university.de) 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. PLOS One | [https://doi.org/10.1371/journal.pone.0321268](https://doi.org/10.1371/journal.pone.0321268) May 28, 2025 1 / 36 awa

FAQs

What is the main objective of the scoping review?

To summarize evidence on how machine learning techniques predict or identify peripartum risk factors related to child development.

How were studies selected and how many were included?

A systematic search was conducted in PubMed, Web of Science, Scopus, and Google Scholar up to January 14, 2025; 10,336 studies were found after duplicates were removed, and 60 studies were included in the final report.

What dimensions were evaluated in the quality assessment?

The appraisal considered representativeness, data leakage, validation, performance metrics, and interpretability, noting that important criteria were not fully met in all studies.

Aug 4, 2026

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