Clustering Relevant Terms and Identifying Types of Statements in Clinical Records

Siklósi, Borbála

doi:10.1007/978-3-319-18117-2_46

Cited by 2 publications

(3 citation statements)

References 15 publications

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“…To leverage the acquired terms for NLP, they are typically organized according to their semantic properties. If the target categories are not yet defined, clustering can be used to group semantically related terms and infer taxonomical relations (Siklósi 2015). However, the more common scenario is that the newly acquired variants need to be integrated into an existing knowledge source.…”

Section: Related Researchmentioning

confidence: 99%

Leveraging Sublanguage Features for the Semantic Categorization of Clinical Terms

Grön

Bertels²,

Heylen³

2019

Proceedings of the 18th BioNLP Workshop and Shared Task

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The automatic processing of clinical documents, such as Electronic Health Records (EHRs), could benefit substantially from the enrichment of medical terminologies with terms encountered in clinical practice. To integrate such terms into existing knowledge sources, they must be linked to corresponding concepts. We present a method for the semantic categorization of clinical terms based on their surface form. We find that features based on sublanguage properties can provide valuable cues for the classification of term variants. 2 https://uts.nlm.nih.gov/home.html

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Section: Related Researchmentioning

confidence: 99%

Leveraging Sublanguage Features for the Semantic Categorization of Clinical Terms

Grön

Bertels²,

Heylen³

2019

Proceedings of the 18th BioNLP Workshop and Shared Task

View full text Add to dashboard Cite

show abstract

“…For example, due to the rare use of verbs, if a past tense verb was recognized in a sentence, it was a good indicator of being part of the anamnesis or the complaints of the patient (Siklósi, 2015).…”

Section: Categorizing Statementsmentioning

confidence: 99%

“…First, using the preprocessed version of the texts, some patterns were identified based on partof-speech tags and the semantic concept categories assigned to the most frequent entities. For example, due to the rare use of verbs, if a past tense verb was recognized in a sentence, it was a good indicator of being part of the anamnesis or the complaints of the patient (Siklósi, 2015).…”

Section: Categorizing Statementsmentioning

confidence: 99%

Restoring the intended structure of Hungarian ophthalmology documents

Siklósi

Novák²

2015

Proceedings of BioNLP 15

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Clinical documents have been an emerging target of natural language applications. Information stored in documents created at clinical settings can be very useful for doctors or medical experts. However, the way these documents are created and stored is often a hindrance to accessing their content. In this paper, an automatic method for restoring the intended structure of Hungarian ophthalmology documents is described. The statements in these documents in their original form appeared under various subheadings. We successfully applied our method for reassigning the correct heading for each line based on its content. The results show that the categorization was correct for 81.99% of the statements in our testset, compared to a human categorization.

show abstract

Clustering Relevant Terms and Identifying Types of Statements in Clinical Records

Cited by 2 publications

References 15 publications

Leveraging Sublanguage Features for the Semantic Categorization of Clinical Terms

Leveraging Sublanguage Features for the Semantic Categorization of Clinical Terms

Restoring the intended structure of Hungarian ophthalmology documents

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