ArticleEnvironmental health perspectives2025
Machine Learning Potential for Identifying and Forecasting Complex Environmental Drivers of
Article in Environmental health perspectives, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Predictive intelligence for future vibriosis risk in the eastern United States employing Bayesian spatial modeling.Applied and environmental microbiology · 2026Article
- It's time to act: Understanding and combatingVirulence · 2025Review
- Machine learning in understanding environmental variability of vibriosis in coastal waters.Applied and environmental microbiology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEnvironmental change in coastal areas can drive marine bacteria and resulting infections, such as those caused by
objectivesThe Cholera and Other
methodsMachine learning models, in the form of random forest classification models, were trained and refined using the epidemiological data from 2008 to 2018, six environmental variables (sea surface temperature, salinity, chlorophyll
resultsThe highest-performing model, which used balanced classes, had an Area Under the Curve score of 0.984 and a sensitivity of 0.971, highlighting the potential of machine learning to anticipate areas and periods of DISCUSSION: The machine learning approach was able to characterize nonlinear and interacting environmental associations driving
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.