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        <title>Detecting Adverse Drug Events from Swedish Electronic Health Records using Text Mining</title>
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    <name type="personal">
        <namePart type="given">Maria</namePart>
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            <title>Proceedings of the LREC 2020 Workshop on Multilingual Biomedical Text Processing (MultilingualBIO 2020)</title>
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            <namePart type="given">Maite</namePart>
            <namePart type="family">Melero</namePart>
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    <abstract>Electronic Health Records are a valuable source of patient information which can be leveraged to detect Adverse Drug Events (ADEs) and aid post-mark drug-surveillance. The overall aim of this study is to scrutinize text written by clinicians in the EHRs and build a model for ADE detection that produces medically relevant predictions. Natural Language Processing techniques will be exploited to create important predictors and incorporate them into the learning process. The study focuses on the 5 most frequent ADE cases found ina Swedish electronic patient record corpus. The results indicate that considering textual features, rather than the structured, can improve the classification performance by 15% in some ADE cases. Additionally, variable patient history lengths are incorporated in the models, demonstrating the importance of the above decision rather than using an arbitrary number for a history length. The experimental findings suggest that the clinical text in EHRs includes information that can capture data beyond the ones that are found in a structured format.</abstract>
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%0 Conference Proceedings
%T Detecting Adverse Drug Events from Swedish Electronic Health Records using Text Mining
%A Bampa, Maria
%A Dalianis, Hercules
%Y Melero, Maite
%S Proceedings of the LREC 2020 Workshop on Multilingual Biomedical Text Processing (MultilingualBIO 2020)
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-65-8
%G English
%F bampa-dalianis-2020-detecting
%X Electronic Health Records are a valuable source of patient information which can be leveraged to detect Adverse Drug Events (ADEs) and aid post-mark drug-surveillance. The overall aim of this study is to scrutinize text written by clinicians in the EHRs and build a model for ADE detection that produces medically relevant predictions. Natural Language Processing techniques will be exploited to create important predictors and incorporate them into the learning process. The study focuses on the 5 most frequent ADE cases found ina Swedish electronic patient record corpus. The results indicate that considering textual features, rather than the structured, can improve the classification performance by 15% in some ADE cases. Additionally, variable patient history lengths are incorporated in the models, demonstrating the importance of the above decision rather than using an arbitrary number for a history length. The experimental findings suggest that the clinical text in EHRs includes information that can capture data beyond the ones that are found in a structured format.
%U https://aclanthology.org/2020.multilingualbio-1.1
%P 1-8

Markdown (Informal)

[Detecting Adverse Drug Events from Swedish Electronic Health Records using Text Mining](https://aclanthology.org/2020.multilingualbio-1.1) (Bampa & Dalianis, MultilingualBIO 2020)

• Detecting Adverse Drug Events from Swedish Electronic Health Records using Text Mining (Bampa & Dalianis, MultilingualBIO 2020)

ACL

• Maria Bampa and Hercules Dalianis. 2020. Detecting Adverse Drug Events from Swedish Electronic Health Records using Text Mining. In Proceedings of the LREC 2020 Workshop on Multilingual Biomedical Text Processing (MultilingualBIO 2020), pages 1–8, Marseille, France. European Language Resources Association.