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 and 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: inconsistent implementation of FAIR principles in data collection and sharing, a lack of skilled data scientists and appropriate cyber-infrastructures, and limited understanding of possibilities and communication of benefits were among those identified. These issues are discussed, and actionable recommendations are provided.
Highlights
Out of the tens of thousands of individual chemicals currently in commerce
The U.S EPA’s Toxic Substances Control Act (TSCA) Chemical Substances Control Inventory contains roughly 85,000 chemicals (U.S EPA, 2016), and the European Chemicals Agency (ECHA) Inventory lists over 100,000 unique substances, of which ∼22,000 are registered substances with some information on structure, usage, or toxicity (ECHA, 2017)
Recent research in toxicology has focused on high-throughput screening to rapidly produce quantitative data on thousands of human biological targets (e.g., Thomas et al, 2019), data-mining to identify relevant end-points building predictive models for adverse toxicological outcomes (e.g., Saili et al, 2019), and application of cutting-edge machine learning (ML) and artificial or augmented intelligence (AI) techniques (e.g., Luechtefeld et al, 2018). These technologies facilitate enhanced mechanistic insights and may obviate the need for inefficient testing in animal models, but they are still not considered mainstream approaches nor are they widely accepted by regulatory agencies
Summary
Out of the tens of thousands of individual chemicals currently in commerce (and many more mixtures, natural products, and metabolites)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have