<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="alimova-tutubalina-2019-detecting">
    <titleInfo>
        <title>Detecting Adverse Drug Reactions from Biomedical Texts with Neural Networks</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Ilseyar</namePart>
        <namePart type="family">Alimova</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Elena</namePart>
        <namePart type="family">Tutubalina</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Fernando</namePart>
            <namePart type="family">Alva-Manchego</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Eunsol</namePart>
            <namePart type="family">Choi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Daniel</namePart>
            <namePart type="family">Khashabi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Florence, Italy</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Detection of adverse drug reactions in postapproval periods is a crucial challenge for pharmacology. Social media and electronic clinical reports are becoming increasingly popular as a source for obtaining health related information. In this work, we focus on extraction information of adverse drug reactions from various sources of biomedical textbased information, including biomedical literature and social media. We formulate the problem as a binary classification task and compare the performance of four state-of-the-art attention-based neural networks in terms of the F-measure. We show the effectiveness of these methods on four different benchmarks.</abstract>
    <identifier type="citekey">alimova-tutubalina-2019-detecting</identifier>
    <identifier type="doi">10.18653/v1/P19-2058</identifier>
    <location>
        <url>https://aclanthology.org/P19-2058</url>
    </location>
    <part>
        <date>2019-07</date>
        <extent unit="page">
            <start>415</start>
            <end>421</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Detecting Adverse Drug Reactions from Biomedical Texts with Neural Networks
%A Alimova, Ilseyar
%A Tutubalina, Elena
%Y Alva-Manchego, Fernando
%Y Choi, Eunsol
%Y Khashabi, Daniel
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F alimova-tutubalina-2019-detecting
%X Detection of adverse drug reactions in postapproval periods is a crucial challenge for pharmacology. Social media and electronic clinical reports are becoming increasingly popular as a source for obtaining health related information. In this work, we focus on extraction information of adverse drug reactions from various sources of biomedical textbased information, including biomedical literature and social media. We formulate the problem as a binary classification task and compare the performance of four state-of-the-art attention-based neural networks in terms of the F-measure. We show the effectiveness of these methods on four different benchmarks.
%R 10.18653/v1/P19-2058
%U https://aclanthology.org/P19-2058
%U https://doi.org/10.18653/v1/P19-2058
%P 415-421

Markdown (Informal)

[Detecting Adverse Drug Reactions from Biomedical Texts with Neural Networks](https://aclanthology.org/P19-2058) (Alimova & Tutubalina, ACL 2019)

• Detecting Adverse Drug Reactions from Biomedical Texts with Neural Networks (Alimova & Tutubalina, ACL 2019)

ACL

• Ilseyar Alimova and Elena Tutubalina. 2019. Detecting Adverse Drug Reactions from Biomedical Texts with Neural Networks. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 415–421, Florence, Italy. Association for Computational Linguistics.