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        <title>Term-Based Extraction of Medical Information: Pre-Operative Patient Education Use Case</title>
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    <name type="personal">
        <namePart type="given">Martin</namePart>
        <namePart type="family">Wolf</namePart>
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        <namePart type="given">Volha</namePart>
        <namePart type="family">Petukhova</namePart>
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    <name type="personal">
        <namePart type="given">Dietrich</namePart>
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            <namePart type="family">Mitkov</namePart>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>The processing of medical information is not a trivial task for medical non-experts. The paper presents an artificial assistant designed to facilitate a reliable access to medical online contents. Interactions are modelled as doctor-patient Question Answering sessions within a pre-operative patient education scenario where the system addresses patient’s information needs explaining medical events and procedures. This implies an accurate medical information extraction from and reasoning with available medical knowledge and large amounts of unstructured multilingual online data. Bridging the gap between medical knowledge and data, we explore a language-agnostic approach to medical concepts mining from the standard terminologies, and the data-driven collection of the corresponding seed terms in a distant supervision setting for German. Experimenting with different terminologies, features and term matching strategies, we achieved a promising F-score of 0.91 on the medical term extraction task. The concepts and terms are used to search and retrieve definitions from the verified online free resources. The proof-of-concept definition retrieval system is designed and evaluated showing promising results, acceptable by humans in 92% of cases.</abstract>
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    <identifier type="doi">10.26615/978-954-452-056-4_154</identifier>
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        <date>2019-09</date>
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%0 Conference Proceedings
%T Term-Based Extraction of Medical Information: Pre-Operative Patient Education Use Case
%A Wolf, Martin
%A Petukhova, Volha
%A Klakow, Dietrich
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F wolf-etal-2019-term
%X The processing of medical information is not a trivial task for medical non-experts. The paper presents an artificial assistant designed to facilitate a reliable access to medical online contents. Interactions are modelled as doctor-patient Question Answering sessions within a pre-operative patient education scenario where the system addresses patient’s information needs explaining medical events and procedures. This implies an accurate medical information extraction from and reasoning with available medical knowledge and large amounts of unstructured multilingual online data. Bridging the gap between medical knowledge and data, we explore a language-agnostic approach to medical concepts mining from the standard terminologies, and the data-driven collection of the corresponding seed terms in a distant supervision setting for German. Experimenting with different terminologies, features and term matching strategies, we achieved a promising F-score of 0.91 on the medical term extraction task. The concepts and terms are used to search and retrieve definitions from the verified online free resources. The proof-of-concept definition retrieval system is designed and evaluated showing promising results, acceptable by humans in 92% of cases.
%R 10.26615/978-954-452-056-4_154
%U https://aclanthology.org/R19-1154
%U https://doi.org/10.26615/978-954-452-056-4_154
%P 1346-1355

Markdown (Informal)

[Term-Based Extraction of Medical Information: Pre-Operative Patient Education Use Case](https://aclanthology.org/R19-1154) (Wolf et al., RANLP 2019)

• Term-Based Extraction of Medical Information: Pre-Operative Patient Education Use Case (Wolf et al., RANLP 2019)

ACL

• Martin Wolf, Volha Petukhova, and Dietrich Klakow. 2019. Term-Based Extraction of Medical Information: Pre-Operative Patient Education Use Case. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 1346–1355, Varna, Bulgaria. INCOMA Ltd..