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Towards Using Scientific Publications to Automatically Extract Information on Rare Diseases

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Abstract

A small percentage of the population is afflicted by what is called an orphan or a rare disease. All over the world, there are about several thousand of these diseases. When adding up together all the individuals who are affected, it amounts for up to 10% of the US population. Scientific works on these diseases are often poorly financed due to the lack of potential markets for a treatment, which means for patients and clinicians a very limited and scattered access to vital information. To contribute addressing this issue, we present in this paper a new software tool for automating the extraction of information related to rare diseases from scientific publications. More precisely, our contribution consists in a new method of extracting automatically symptoms of these diseases from research papers exploiting a Named Entity Recognition (NER) algorithm based on the numerical statistic Term Frequency - Inverse Document Frequency (TF-IDF). The proposed tool has been tested using PubMed Central (PMC) database.

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Notes

  1. https://rarediseases.info.nih.gov/diseases/pages/31/faqs-about-rare-diseases

  2. https://sites.google.com/site/bionlpst/home/geniaevent-extraction-genia

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Acknowledgements

This project success was conducted with the financial support received from UQAC and the National Sciences and Engineering Research Council of Canada (NSERC).

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Authors and Affiliations

  1. LIARA Laboratory, Université du Québec à Chicoutimi, Chicoutimi, QC, G7H 2B1, Canada

    Charles Cousyn, Kévin Bouchard, Sébastien Gaboury & Bruno Bouchard

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  1. Charles Cousyn
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  2. Kévin Bouchard
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  3. Sébastien Gaboury
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  4. Bruno Bouchard
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Correspondence to Charles Cousyn.

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Cousyn, C., Bouchard, K., Gaboury, S. et al. Towards Using Scientific Publications to Automatically Extract Information on Rare Diseases. Mobile Netw Appl 25, 953–960 (2020). https://doi.org/10.1007/s11036-019-01237-3

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