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        <title>Comparing the Intrinsic Performance of Clinical Concept Embeddings by Their Field of Medicine</title>
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        <namePart type="given">John-Jose</namePart>
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            <title>Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)</title>
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            <namePart type="given">Eben</namePart>
            <namePart type="family">Holderness</namePart>
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        <name type="personal">
            <namePart type="given">Antonio</namePart>
            <namePart type="family">Jimeno Yepes</namePart>
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        <name type="personal">
            <namePart type="given">Alberto</namePart>
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        <name type="personal">
            <namePart type="given">Anne-Lyse</namePart>
            <namePart type="family">Minard</namePart>
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        <name type="personal">
            <namePart type="given">James</namePart>
            <namePart type="family">Pustejovsky</namePart>
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        <name type="personal">
            <namePart type="given">Fabio</namePart>
            <namePart type="family">Rinaldi</namePart>
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    <abstract>Pre-trained word embeddings are becoming increasingly popular for natural language processing tasks. This includes medical applications, where embeddings are trained for clinical concepts using specific medical data. Recent work continues to improve on these embeddings. However, no one has yet sought to determine whether these embeddings work as well for one field of medicine as they do in others. In this work, we use intrinsic methods to evaluate embeddings from the various fields of medicine as defined by their ICD-9 systems. We find significant differences between fields, and motivate future work to investigate whether extrinsic tasks will follow a similar pattern.</abstract>
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%0 Conference Proceedings
%T Comparing the Intrinsic Performance of Clinical Concept Embeddings by Their Field of Medicine
%A Nunez, John-Jose
%A Carenini, Giuseppe
%Y Holderness, Eben
%Y Jimeno Yepes, Antonio
%Y Lavelli, Alberto
%Y Minard, Anne-Lyse
%Y Pustejovsky, James
%Y Rinaldi, Fabio
%S Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong
%F nunez-carenini-2019-comparing
%X Pre-trained word embeddings are becoming increasingly popular for natural language processing tasks. This includes medical applications, where embeddings are trained for clinical concepts using specific medical data. Recent work continues to improve on these embeddings. However, no one has yet sought to determine whether these embeddings work as well for one field of medicine as they do in others. In this work, we use intrinsic methods to evaluate embeddings from the various fields of medicine as defined by their ICD-9 systems. We find significant differences between fields, and motivate future work to investigate whether extrinsic tasks will follow a similar pattern.
%R 10.18653/v1/D19-6202
%U https://aclanthology.org/D19-6202
%U https://doi.org/10.18653/v1/D19-6202
%P 11-17

Markdown (Informal)

[Comparing the Intrinsic Performance of Clinical Concept Embeddings by Their Field of Medicine](https://aclanthology.org/D19-6202) (Nunez & Carenini, Louhi 2019)

• Comparing the Intrinsic Performance of Clinical Concept Embeddings by Their Field of Medicine (Nunez & Carenini, Louhi 2019)

ACL

• John-Jose Nunez and Giuseppe Carenini. 2019. Comparing the Intrinsic Performance of Clinical Concept Embeddings by Their Field of Medicine. In Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019), pages 11–17, Hong Kong. Association for Computational Linguistics.