Abstract
The generation of medical terminologies is an important activity. A flexible and structured terminology both helps professionals in everyday manual classification of clinical texts and is crucial to build knowledge bases for encoding tools implementing software to support medical tasks. For these reasons, it would be nice to “enforce” medical dictionaries such as MedDRA with sets of locutions semantically related to official terms. Unfortunately, the manual generation of medical terminologies is time consuming. Even if the human validation is an irreplaceable step, a significative set of “high-quality” candidate terminologies can be automatically generated from clinical documents by statistical methods for linguistic. In this paper we adapt and use a co-occurrence based technique to generate new MedDRA locutions, starting from some large sets of narrative documents about adverse drug reactions. We describe here the methodology we designed and results of some first experiments.
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Notes
- 1.
SPCs are available at https://farmaci.agenziafarmaco.gov.it/bancadatifarmaci/.
- 2.
The choice of k depends on the average length of the documents in the datasets.
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Zorzi, M., Combi, C., Pozzani, G., Arzenton, E., Moretti, U. (2017). A Co-occurrence Based MedDRA Terminology Generation: Some Preliminary Results. In: ten Teije, A., Popow, C., Holmes, J., Sacchi, L. (eds) Artificial Intelligence in Medicine. AIME 2017. Lecture Notes in Computer Science(), vol 10259. Springer, Cham. https://doi.org/10.1007/978-3-319-59758-4_24
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DOI: https://doi.org/10.1007/978-3-319-59758-4_24
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