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Updated: Aug 31, 2025

A Web Tool for Generating High Quality Machine-readable Biological Pathways

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

Using Artificial Intelligence to extract information on pathogen characteristics from scientific publications.

Sotirios Paraskevopoulos¹, Patrick Smeets², Xin Tian²

¹KWR Water Research Institute, Groningenhaven 7, P.O. Box 1072, 3430 BB, Nieuwegein, the Netherlands; Department of Water Management, Delft University of Technology, Stevinweg 1, 2628, CN Delft, the Netherlands.

International Journal of Hygiene and Environmental Health

|August 19, 2022

Summary

Artificial Intelligence (AI) can efficiently extract Legionella data from scientific literature. This automated approach using Deep Learning and Natural Language Processing matches manual review accuracy for health risk assessments.

Keywords:

Artificial intelligence Exposure assessment Information extraction Legionella Scientific publications

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Area of Science:

Environmental microbiology
Bioinformatics
Public health

Background:

Accurate health risk assessments depend on comprehensive pathogen data.
The increasing volume of scientific literature necessitates efficient review methods.
Automated information extraction using AI can streamline literature reviews.

Purpose of the Study:

To assess the feasibility of using AI to extract qualitative and quantitative data on Legionella from PubMed publications.
To evaluate the performance of Deep Learning and Natural Language Processing techniques for this extraction task.

Main Methods:

Developed and applied a Deep Learning and Natural Language Processing model.
Extracted qualitative and quantitative information on Legionella from scientific literature.
Compared AI-driven extraction results with manual information extraction.

Main Results:

The AI model achieved high precision (0.91), recall (0.80), and F-score (0.85).
AI extraction performance was comparable to manual information extraction.
The model reliably extracted both qualitative and quantitative Legionella characteristics.

Conclusions:

AI, specifically Deep Learning and Natural Language Processing, can reliably extract critical data on Legionella from scientific publications.
This automated approach enhances the efficiency of literature reviews in environmental microbiology.
The study represents a significant step towards leveraging AI for pathogen data collection.