Abstract
In this paper, we tackle the issue of symptom recognition for rare diseases in biomedical texts. Symptoms typically have more complex and ambiguous structure than other biomedical named entities. Furthermore, existing resources are scarce and incomplete. Therefore, we propose a weakly-supervised framework based on a combination of two approaches: sequential pattern mining under constraints and sequence labeling. We use unannotated biomedical paper abstracts with dictionaries of rare diseases and symptoms to create our training data. Our experiments show that both approaches outperform simple projection of the dictionaries on text, and their combination is beneficial. We also introduce a novel pattern mining constraint based on semantic similarity between words inside patterns.
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Using the script provided by http://www.cnts.ua.ac.be/conll2000/chunking/output.html which take the same input data format (BIO) as our data.
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Acknowledgments
This work is supported by the French National Research Agency (ANR) as part of the project Hybride ANR-11-BS02-002 and the “Investissements d’Avenir” program (reference: ANR-10-LABX-0083).
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Holat, P., Tomeh, N., Charnois, T., Battistelli, D., Jaulent, MC., Métivier, JP. (2016). Weakly-Supervised Symptom Recognition for Rare Diseases in Biomedical Text. In: Boström, H., Knobbe, A., Soares, C., Papapetrou, P. (eds) Advances in Intelligent Data Analysis XV. IDA 2016. Lecture Notes in Computer Science(), vol 9897. Springer, Cham. https://doi.org/10.1007/978-3-319-46349-0_17
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