FindZebra online search delving into rare disease case reports using natural language processing

Valentin Liévin, Jonas Meinertz Hansen, Allan Lund, Deborah Elstein, Mads Emil Matthiesen, Kaisa Elomaa, Kaja Zarakowska, Iris Himmelhan, Jaco Botha, Hanne Borgeskov, Ole Winther*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

4 Citations (Scopus)
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Abstract

Early diagnosis is crucial for well-being and life quality of the rare disease patient. Access to the most complete knowledge about diseases through intelligent user interfaces can play an important role in supporting the physician reaching the correct diagnosis. Case reports may offer information about heterogeneous phenotypes which often further complicate rare disease diagnosis. The rare disease search engine FindZebra.com is extended to also access case report abstracts extracted from PubMed for several diseases. A search index for each disease is built in Apache Solr adding age, sex and clinical features extracted using text segmentation to enhance the specificity of search. Clinical experts performed retrospective validation of the search engine, utilising real-world Outcomes Survey data on Gaucher and Fabry patients. Medical experts evaluated the search results as being clinically relevant for the Fabry patients and less clinically relevant for the Gaucher patients. The shortcomings for Gaucher patients mainly reflect a mismatch between the current understanding and treatment of the disease and how it is reported in PubMed, notably in the older case reports. In response to this observation, a filter for the publication date was added in the final version of the tool available from deep.findzebra.com/<disease> with <disease> = gaucher, fabry, hae (Hereditary angioedema).

Original languageEnglish
Article numbere0000269
JournalPLOS Digital Health
Volume2
Issue number6
Number of pages14
DOIs
Publication statusPublished - 2023

Bibliographical note

Publisher Copyright:
Copyright: © 2023 Liévin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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