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Machine Learning Within Studies of Early-Life Environmental Exposures and Child Health: Review of the Current Literature and Discussion of Next Steps

  • Early Life Environmental Health (H Volk & J Buckley, Section Editors)
  • Published:
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Abstract

Purpose of Review

The goal of this article is to review the use of machine learning (ML) within studies of environmental exposures and children’s health, identify common themes across studies, and provide recommendations to advance their use in research and practice.

Recent Findings

We identified 42 articles reporting upon the use of ML within studies of environmental exposures and children’s health between 2017 and 2019. The common themes among the articles were analysis of mixture data, exposure prediction, disease prediction and forecasting, analysis of complex data, and causal inference.

Summary

With the increasing complexity of environmental health data, we anticipate greater use of ML to address the challenges that cannot be handled by traditional analytics. In order for these methods to beneficially impact public health, the ML techniques we use need to be appropriate for our study questions, rigorously evaluated and reported in a way that can be critically assessed by the scientific community.

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Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

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  1. Department of Epidemiology, Columbia University Mailman School of Public Health, 722 West 168th St, Room 1608, New York, NY, 10032, USA

    Sabine Oskar & Jeanette A. Stingone

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  1. Sabine Oskar
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Correspondence to Jeanette A. Stingone.

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Dr. Stingone reports funding from the NIEHS (ES027022) during the conduct of the study. The other author declares that there is no conflict of interest.

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All reported studies/experiments with human or animal subjects performed by the authors have been previously published and complied with all applicable ethical standards (including the Helsinki declaration and its amendments, institutional/national research committee standards, and international/national/institutional guidelines).

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Oskar, S., Stingone, J.A. Machine Learning Within Studies of Early-Life Environmental Exposures and Child Health: Review of the Current Literature and Discussion of Next Steps. Curr Envir Health Rpt 7, 170–184 (2020). https://doi.org/10.1007/s40572-020-00282-5

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