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Mapping disease-environment connections reported in scientific literature at scale with EasyNER

Aasa, Carl ; Boyd, Emily LU and Aits, Sonja LU orcid (2025) EurIPS 2025
Abstract
Climate change and connected environmental disruptions have a large negative
effect on the health of humans and other species. Improved understanding of
known links in the environment-disease nexus as well as knowledge gaps is
essential to prepare for and mitigate this escalating threat. However, knowledge
fragments are scattered across millions of scientific articles making information
synthesis too complex even for large review consortia. Natural language
processing (NLP) tools could help execute this at scale. Here, we developed an
information synthesis pipeline for mapping links between environmental
processes and diseases which expanded our EasyNER tool. We then applied it to
extract co-mentions... (More)
Climate change and connected environmental disruptions have a large negative
effect on the health of humans and other species. Improved understanding of
known links in the environment-disease nexus as well as knowledge gaps is
essential to prepare for and mitigate this escalating threat. However, knowledge
fragments are scattered across millions of scientific articles making information
synthesis too complex even for large review consortia. Natural language
processing (NLP) tools could help execute this at scale. Here, we developed an
information synthesis pipeline for mapping links between environmental
processes and diseases which expanded our EasyNER tool. We then applied it to
extract co-mentions from 16.7 million abstracts in PubMed, a large collection of
life science literature, producing a knowledge graph of the environment-disease
nexus. (Less)
Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Contribution to conference
publication status
published
subject
keywords
natural language processing, Named entity recognition, environmental health, environmental toxicology, Biodiversity, Climate change, Artificial intelligence, Deep learning, Machine learning, software, BioBERT
conference name
EurIPS 2025
conference location
Copenhagen, Denmark
conference dates
2025-12-02 - 2025-12-07
project
Biomedical text mining for systems biology
The Swedish Centre of Excellence on Impacts of Climate Extremes (Climes)
Artificial intelligence-based text mining for COVID-19 and other areas of medicine
Environmental toxins as cause of lysosomal damage-induced cell death in animals and humans
Lund University AI Research
Revealing drivers of cell death disruption across species and theri links to biodiversity loss and human disease
Lysosomes in cell death - from molecular mechanisms to new treatment strategies
language
English
LU publication?
yes
id
7e44ba10-56ae-4b56-bf9e-d2b61a1eeecb
alternative location
https://github.com/climateainordics/AICC2025/blob/main/papers/Mapping_Aasa.pdf
date added to LUP
2026-01-15 08:32:55
date last changed
2026-01-15 08:46:28
@misc{7e44ba10-56ae-4b56-bf9e-d2b61a1eeecb,
  abstract     = {{Climate change and connected environmental disruptions have a large negative<br/>effect on the health of humans and other species. Improved understanding of<br/>known links in the environment-disease nexus as well as knowledge gaps is<br/>essential to prepare for and mitigate this escalating threat. However, knowledge<br/>fragments are scattered across millions of scientific articles making information<br/>synthesis too complex even for large review consortia. Natural language<br/>processing (NLP) tools could help execute this at scale. Here, we developed an<br/>information synthesis pipeline for mapping links between environmental<br/>processes and diseases which expanded our EasyNER tool. We then applied it to<br/>extract co-mentions from 16.7 million abstracts in PubMed, a large collection of<br/>life science literature, producing a knowledge graph of the environment-disease<br/>nexus.}},
  author       = {{Aasa, Carl and Boyd, Emily and Aits, Sonja}},
  keywords     = {{natural language processing; Named entity recognition; environmental health; environmental toxicology; Biodiversity; Climate change; Artificial intelligence; Deep learning; Machine learning; software; BioBERT}},
  language     = {{eng}},
  month        = {{12}},
  title        = {{Mapping disease-environment connections reported in scientific literature at scale with EasyNER}},
  url          = {{https://github.com/climateainordics/AICC2025/blob/main/papers/Mapping_Aasa.pdf}},
  year         = {{2025}},
}