Journal:Bringing big data to bear in environmental public health: Challenges and recommendations

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Full article title Bringing big data to bear in environmental public health: Challenges and recommendations
Journal Frontiers in Artificial Intelligence
Author(s) Comess, Saskia; Akbay, Alexia; Vasiliou, Melpomene; Hines, Ronald N.; Joppa, Lucas; Vasiliou, Vasilis; Kleinstreuer, Nicole
Author affiliation(s) Yale University, Symbrosia Inc., U.S. EPA, Microsoft Corporation, National Institute of Environmental Health Sciences
Primary contact Email: vasilis dot vasiliou at yale dot edu and nicole dot kleinstreuer at nih dot gov
Editors Emmert-Streib, Frank
Year published 2020
Volume and issue 3
Page(s) 31
DOI 10.3389/frai.2020.00031
ISSN 2624-8212
Distribution license Creative Commons Attribution 4.0 International
Website https://www.frontiersin.org/articles/10.3389/frai.2020.00031/full
Download https://www.frontiersin.org/articles/10.3389/frai.2020.00031/pdf (PDF)

Abstract

Understanding the role that the environment plays in influencing public health often involves collecting and studying large, complex data sets. There have been a number of private and public efforts to gather sufficient information and confront significant unknowns in the field of environmental public health, yet there is a persistent and largely unmet need for findable, accessible, interoperable, and reusable (FAIR) data. Even when data are readily available, the ability to create, analyze, and draw conclusions from these data using emerging computational tools, such as augmented intelligence, artificial intelligence (AI), and machine learning, requires technical skills not currently implemented on a programmatic level across research hubs and academic institutions. We argue that collaborative efforts in data curation and storage, scientific computing, and training are of paramount importance to empower researchers within environmental sciences and the broader public health community to apply AI approaches and fully realize their potential. Leaders in the field were asked to prioritize challenges in incorporating big data in environmental public health research, including inconsistent implementation of FAIR principles in data collection and sharing; a lack of skilled data scientists and appropriate cyber-infrastructures; and limited understanding, identification, and communication of benefits. These issues are discussed and actionable recommendations are provided.

Keywords: artificial intelligence, public health, machine learning, open data, environmental health sciences, big data

Introduction

Out of the tens of thousands of individual chemicals currently in commerce (and many more mixtures, natural products, and metabolites) less than 10 percent have been screened for safety. The United States Environmental Protection Agency's (EPA's) Toxic Substances Control Act (TSCA) Chemical Substance Inventory contains roughly 85,000 chemicals[1], and the European Chemicals Agency (ECHA) Inventory lists over 100,000 unique substances (as of the most recent update in August 2017), of which approximately 22,000 are registered substances with detailed information on structure, usage, or toxicity.[2] Understanding which chemicals in the environment—both with and without safety data—pose a risk to human health requires that we more effectively leverage the data that we already have, and that we take intelligent approaches to generating new data. While the traditional means of collecting chemical safety data (animal models) are laborious and of variable accuracy and human relevance[3], such reference data can still be used to train models for prioritizing and predicting toxicity of new chemicals, provided the data are curated in a computationally accessible format and, ideally, integrated with other lines of evidence providing mechanistic information. This requires significant effort, both in collecting and extracting information as well as annotating it appropriately.



References

  1. Environmental Protection Agency. "TSCA Chemical Substance Inventory: About the TSCA Chemical Substance Inventory". Environmental Protection Agency. https://www.epa.gov/tsca-inventory/about-tsca-chemical-substance-inventory. Retrieved 04 January 2019. 
  2. European Chemicals Agency. "EC Inventory". European Chemicals Agency. https://echa.europa.eu/information-on-chemicals/ec-inventory. Retrieved 04 January 2019. 
  3. Hartung, T. (2009). "Toxicology for the Twenty-First Century". Nature 460 (7252): 208–12. doi:10.1038/460208a. PMID 19587762. 

Notes

This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added. The original article lists references alphabetically, but this version—by design—lists them in order of appearance.