Machine Learning with FLIT-SHAP Reveals
Crucial Pollutant Interactions, Enhancing Toxicity Predictions and
Environmental Health Decision-Making
BUSAN,
South Korea, July 23,
2024 /PRNewswire/ -- Traditional environmental health
research often focuses on the toxicity of single chemical
exposures. However, in real-world situations, people are exposed to
multiple pollutants simultaneously, which can interact in complex
ways, potentially amplifying or diminishing their toxic effects.
Conventional models that assume additive effects, like
concentration addition and independent action, can be misleading in
these scenarios. Although advanced statistical and machine learning
methods have been employed to address this issue, they frequently
fall short due to the complexity, high number of interacting
pollutants and the inability to extract each pollutant's absolute
effect.
To address this issue, a group of researchers led by Professor
Kuk Cho from Pusan National University introduced Feature Localized
Intercept Transformed-Shapley Additive Explanations (FLIT-SHAP) as
a solution to these challenges. This tool is unique because it
breaks down the effects of specific pollutants within a mixture,
unlike traditional methods that use a broader approach. This
detail-oriented technique is particularly useful for understanding
the OP of particulate matter (PM) in the air, which is known to
cause various health problems. This study was published in Volume
475 of the Journal of Hazardous Materials, on 14 August 2024.
"The OP of PM refers to the ability of these particles to
generate reactive oxygen species, which can cause oxidative stress
and inflammation in the body, leading to various health problems
such as respiratory and cardiovascular diseases," explains
Prof. Cho. The scientists use FLIT-SHAP to examine the OP of these
particles in a mixture of pollutants. Laboratory-controlled data
was used to train robust machine learning models like eXtreme
Gradient Boosting (XGBoost) and their FLIT-SHAP explanation tool
was used to reveal significant insights.
"We found that in a controlled laboratory environment, 55-63%
of interactions were synergistic (meaning they work together to
increase toxicity), while 25-42% were antagonistic (where they
counteract each other). However, in real-world scenarios,
antagonistic effects were more common, leading to lower overall
toxicity than predicted by older models," added Prof. Cho.
One surprising finding was the significant impact of a group of
chemicals called quinones, which showed more contribution
to toxicity than previously thought. This insight
challenges current approaches to regulating pollutants and suggests
a greater need to focus on these chemicals. FLIT-SHAP not only
predicted toxicity with remarkable accuracy (R² = 0.99) but also
offered detailed insights into how different pollutants interact.
This makes it a powerful tool for conducting realistic risk
assessments and enhancing our understanding of environmental health
risks.
The researchers' rigorous tests uncovered how pollutants
interact at varying concentrations, revealing that traditional
additive models often over- or under-estimate mixture toxicity.
Through FLIT-SHAP, they pinpointed crucial nuances in pollutant
contributions across different environments, underscoring the need
for refined risk assessments.
While the study focused on a select group of pollutants and
toxicity endpoint, further research into broader scenarios is
essential. FLIT-SHAP represents a significant leap forward,
offering a precise tool to evaluate chemical mixture toxicity. This
advancement promises better regulatory decisions to safeguard human
health effectively
Reference
Title of original paper: Machine learning-derived dose-response
relationships considering interactions in mixtures: Applications to
the oxidative potential of particulate matter
Journal: Journal of Hazardous Materials
DOI: https://doi.org/10.1016/j.jhazmat.2024.134864
About the institute
Website:
https://www.pusan.ac.kr/eng/Main.do
Contact:
Jae-Eun
Lee
82 51 510 7928
380682@email4pr.com
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SOURCE Pusan National University