Leveraging Artificial Intelligence and Machine Learning to Advance Environmental Health Research and Decisions Proceedings of a Workshop—in Brief (2019) / Chapter Skim
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Leveraging Artificial Intelligence and Machine Learning to Advance Environmental Health Research and Decisions: Proceedings of a Workshop - in Brief
Leveraging Artificial Intelligence and Machine Learning to Advance Environmental Health Research and Decisions: Proceedings of a Workshop - in Brief
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Speakers highlighted the use of AI and machine learning to characterize sources of pollution, predict chemical toxicity, and estimate human exposures to contaminants, among other applications. Though promising, questions remain about the use of AI and machine learning in environmental health research and public policy decisions. For example, workshop participants examined how a lack of transparency and interpretability of AI systems compounds fundamental issues about the availability, quality, bias, and uncertainty in the data used to develop machine learning algorithms. Participants also discussed how these issues may impact the reproducibility and replicability of results, deliver misleading or inaccurate results, and potentially diminish social trust in research.
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Richard Woychik of NIEHS highlighted five areas of environmental health for which AI and machine learning could play an integral role in research: predicting the toxicology of chemicals, measuring the exposome,3 understanding the interactions between genes and environmental exposures, examining the role of epigenetics, and supporting systematic reviews4 of scientific literature. Woychik imagined a future in which AI could integrate environmental health datasets -- such as curated legacy data, results of high-throughput screening assays, and details about specific chemical features -- and provide "everything we need to know about various different chemicals," within a few hours.
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However, the two approaches are not mutually exclusive, and using both types to address the same problem can provide complementary information. PREDICTING CHEMICAL TOXICITY: THE APPLICATION OF AI TO CHEMICAL HAZARDS CHARACTERIZATION To characterize chemical hazards, Thomas Luechtefeld from Insilica developed a large neural network that breaks down a given chemical of interest into various functional groups and other features and uses those to produce globally recognized chemical hazard labels.
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Machine learning approaches, though, are well positioned to accommodate high dimensionality and complexity in data structures while providing the flexibility to capture non-linear interactions among chemicals in a mixture. Doing so, said Kioumourtzoglou, requires understanding the strengths and limitations of the many available machine learning techniques, as well as having environmental epidemiologists and toxicologists collaborating with machine learning experts and biostatisticians to tweak, adapt, and extend existing methods to make them more appropriate for use in environmental health studies, particularly regarding interpretability and robustness.
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Such a model could be valuable for triaging patients -- enabling clinicians to determine which individuals need palliative care and which need survivor serum and other treatments to overcome infection, she explained. When asked about how they address data quality, Waters, Kioumourtzoglou, and Kleinstreuer said they model uncertainty; perform many hours of manual curation; run consensus models to override data errors; use standardized pipelines for processing, normalizing, and analyzing data; and use statistically driven imputation approaches to fill in missing data.
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One approach Weichenthal is exploring to address those limitations is to use large databases of images of pollution to predict environmental exposures.15 This approach assumes that the built environment plays a large role in exposures and that photographs and satellite images of the built environment therefore contain exposure information. For the analysis, he uses a deep convolutional neural network16 that extracts information by analyzing layers within an image, makes predictions, compares the predicted results to the true value, adjusts the filters the network uses to extract information from an image, and then repeats this process iteratively to arrive at a best prediction.
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Wambaugh then applied the consensus model to predict the exposure pathway and intake rates for more than 687,000 chemicals in EPA's database that have minimal exposure information. While it seems unlikely to extrapolate exposure information about 687,000 chemicals with a machine learning model developed from data on 120 chemicals, Wambaugh and his colleagues were able determine exposure pathways for 70 percent of the chemicals.
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Hands-on Learning Experience To get a sense of how to use machine learning to answer questions regarding chemical exposure and environmental health, the audience participated in a hands-on learning experience. To provide that experience, David Dunson of Duke University and two of his students, Kelly Moran and Evan Poworoznek, guided the workshop participants through a demonstration of machine learning using ToxCast data on chemical features and chemical dose–responses to predict toxicity outcomes from chemicals that have yet to be studied.
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In summary, Tropsha said that while the accumulation of big data created previously unachievable opportunities for using AI and machine learning approaches, it is critically important to curate and validate the primary data with extreme care. He noted that the growing use of models to guide experimental research raises the importance of rigorous and comprehensive model validation using truly external data.
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After validation with a small subset of homes from which water samples were collected, this algorithm identified 100 percent of the contaminated wells but with a 40 percent false positive rate. Lowit and Rasoulpour commented that while false positives can be an acceptable tradeoff for protecting public health, a false positive rate of that magnitude can lead to the public lacking trust in science.
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She also pointed out that important regulatory decisions are not made based on individual pieces of information, and in that respect, AI and machine learning are not meant to be the end-all and be-all for decision makers. CLOSING THOUGHTS AI and machine learning "really do have the potential to revolutionize environmental health," stated Gary Miller of Columbia University in his closing remarks for the workshop.
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The statements made are those of the rapporteurs or individual workshop participants and do not necessarily represent the views of all workshop participants, the planning committee, or the National Academies of Sciences, Engineering, and Medicine. Planning Committee on Leveraging Artificial Intelligence and Machine Learning to Advance Environmental Health Research and Decisions: A Workshop Kevin Elliott, Michigan State University; Nicole Kleinstreuer, National Institutes of Health; Patrick McMullen, ScitoVation; Gary Miller, Columbia University; Bhramar Mukherjee, University of Michigan; Roger D
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