Does Enrichment of Clinical Texts by Ontology Concepts Increases Classification Accuracy?...18th World Congress of Medical and Health Informatics, MedInfo 2021 - One World, One Health – Global Partnership for Digital Innovation, 2-4 October, 2021.
In the medical domain, multiple ontologies and terminology systems are available. However, existing classification and prediction algorithms in the clinical domain often ignore or insufficiently utilize semantic information as it is provided in those ontologies. To address this issue, we introduce a...
| Published in: | Studies in Health Technology & Informatics Vol. 290; pp. 602 - 607 |
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| Format: | abstract proceedings tables/charts Journal Article |
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Sage Publications Inc.
2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=157572023&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157572023 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2022 vid: 290 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 157572023 157572023 157572023 10.3233/SHTI220148 157572023 ppf: 602 ppct: 5 formats: tig: atl: Does Enrichment of Clinical Texts by Ontology Concepts Increases Classification Accuracy?...18th World Congress of Medical and Health Informatics, MedInfo 2021 - One World, One Health – Global Partnership for Digital Innovation, 2-4 October, 2021. aug: au: Denecke, Kerstin affil: Institute for Medical Informatics, Bern University of Applied Sciences, Bern, Switzerland sug: subj: Machine Learning Semantics Natural Language Processing Ontologies Classification Congresses and Conferences ab: In the medical domain, multiple ontologies and terminology systems are available. However, existing classification and prediction algorithms in the clinical domain often ignore or insufficiently utilize semantic information as it is provided in those ontologies. To address this issue, we introduce a concept for augmenting embeddings, the input to deep neural networks, with semantic information retrieved from ontologies. To do this, words and phrases of sentences are mapped to concepts of a medical ontology aggregating synonyms in the same concept. A semantically enriched vector is generated and used for sentence classification. We study our approach on a sentence classification task using a real world dataset which comprises 640 sentences belonging to 22 categories. A deep neural network model is defined with an embedding layer followed by two LSTM layers and two dense layers. Our experiments show, classification accuracy without content enriched embeddings is for some categories higher than without enrichment. We conclude that semantic information from ontologies has potential to provide a useful enrichment of text. Future research will assess to what extent semantic relationships from the ontology can be used for enrichment. pubtype: Academic Journal doctype: abstract proceedings tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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