Journal:Bringing big data to bear in environmental public health: Challenges and recommendations
| 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) |
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This article should be considered a work in progress and incomplete. Consider this article incomplete until this notice is removed. |
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
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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.
