<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="yamashita-etal-2016-prediction">
    <titleInfo>
        <title>Prediction of Key Patient Outcome from Sentence and Word of Medical Text Records</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Takanori</namePart>
        <namePart type="family">Yamashita</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yoshifumi</namePart>
        <namePart type="family">Wakata</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Hidehisa</namePart>
        <namePart type="family">Soejima</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Naoki</namePart>
        <namePart type="family">Nakashima</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sachio</namePart>
        <namePart type="family">Hirokawa</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2016-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Anna</namePart>
            <namePart type="family">Rumshisky</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kirk</namePart>
            <namePart type="family">Roberts</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Steven</namePart>
            <namePart type="family">Bethard</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Tristan</namePart>
            <namePart type="family">Naumann</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>The COLING 2016 Organizing Committee</publisher>
            <place>
                <placeTerm type="text">Osaka, Japan</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>The number of unstructured medical records kept in hospital information systems is increasing. The conditions of patients are formulated as outcomes in clinical pathway. A variance of an outcome describes deviations from standards of care like a patient’s bad condition. The present paper applied text mining to extract feature words and phrases of the variance from admission records. We report the cases the variances of “pain control” and “no neuropathy worsening” in cerebral infarction.</abstract>
    <identifier type="citekey">yamashita-etal-2016-prediction</identifier>
    <location>
        <url>https://aclanthology.org/W16-4212</url>
    </location>
    <part>
        <date>2016-12</date>
        <extent unit="page">
            <start>86</start>
            <end>90</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Prediction of Key Patient Outcome from Sentence and Word of Medical Text Records
%A Yamashita, Takanori
%A Wakata, Yoshifumi
%A Soejima, Hidehisa
%A Nakashima, Naoki
%A Hirokawa, Sachio
%Y Rumshisky, Anna
%Y Roberts, Kirk
%Y Bethard, Steven
%Y Naumann, Tristan
%S Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F yamashita-etal-2016-prediction
%X The number of unstructured medical records kept in hospital information systems is increasing. The conditions of patients are formulated as outcomes in clinical pathway. A variance of an outcome describes deviations from standards of care like a patient’s bad condition. The present paper applied text mining to extract feature words and phrases of the variance from admission records. We report the cases the variances of “pain control” and “no neuropathy worsening” in cerebral infarction.
%U https://aclanthology.org/W16-4212
%P 86-90

Markdown (Informal)

[Prediction of Key Patient Outcome from Sentence and Word of Medical Text Records](https://aclanthology.org/W16-4212) (Yamashita et al., ClinicalNLP 2016)

• Prediction of Key Patient Outcome from Sentence and Word of Medical Text Records (Yamashita et al., ClinicalNLP 2016)

ACL

• Takanori Yamashita, Yoshifumi Wakata, Hidehisa Soejima, Naoki Nakashima, and Sachio Hirokawa. 2016. Prediction of Key Patient Outcome from Sentence and Word of Medical Text Records. In Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP), pages 86–90, Osaka, Japan. The COLING 2016 Organizing Committee.