<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="wang-etal-2020-personalized">
    <titleInfo>
        <title>Personalized Early Stage Alzheimer’s Disease Detection: A Case Study of President Reagan’s Speeches</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Ning</namePart>
        <namePart type="family">Wang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Fan</namePart>
        <namePart type="family">Luo</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Vishal</namePart>
        <namePart type="family">Peddagangireddy</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Koduvayur</namePart>
        <namePart type="family">Subbalakshmi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Rajarathnam</namePart>
        <namePart type="family">Chandramouli</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Dina</namePart>
            <namePart type="family">Demner-Fushman</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kevin</namePart>
            <namePart type="given">Bretonnel</namePart>
            <namePart type="family">Cohen</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Sophia</namePart>
            <namePart type="family">Ananiadou</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Junichi</namePart>
            <namePart type="family">Tsujii</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Online</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Alzheimer’s disease (AD)-related global healthcare cost is estimated to be $1 trillion by 2050. Currently, there is no cure for this disease; however, clinical studies show that early diagnosis and intervention helps to extend the quality of life and inform technologies for personalized mental healthcare. Clinical research indicates that the onset and progression of Alzheimer’s disease lead to dementia and other mental health issues. As a result, the language capabilities of patient start to decline. In this paper, we show that machine learning-based unsupervised clustering of and anomaly detection with linguistic biomarkers are promising approaches for intuitive visualization and personalized early stage detection of Alzheimer’s disease. We demonstrate this approach on 10 year’s (1980 to 1989) of President Ronald Reagan’s speech data set. Key linguistic biomarkers that indicate early-stage AD are identified. Experimental results show that Reagan had early onset of Alzheimer’s sometime between 1983 and 1987. This finding is corroborated by prior work that analyzed his interviews using a statistical technique. The proposed technique also identifies the exact speeches that reflect linguistic biomarkers for early stage AD.</abstract>
    <identifier type="citekey">wang-etal-2020-personalized</identifier>
    <identifier type="doi">10.18653/v1/2020.bionlp-1.14</identifier>
    <location>
        <url>https://aclanthology.org/2020.bionlp-1.14</url>
    </location>
    <part>
        <date>2020-07</date>
        <extent unit="page">
            <start>133</start>
            <end>139</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Personalized Early Stage Alzheimer’s Disease Detection: A Case Study of President Reagan’s Speeches
%A Wang, Ning
%A Luo, Fan
%A Peddagangireddy, Vishal
%A Subbalakshmi, Koduvayur
%A Chandramouli, Rajarathnam
%Y Demner-Fushman, Dina
%Y Cohen, Kevin Bretonnel
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F wang-etal-2020-personalized
%X Alzheimer’s disease (AD)-related global healthcare cost is estimated to be $1 trillion by 2050. Currently, there is no cure for this disease; however, clinical studies show that early diagnosis and intervention helps to extend the quality of life and inform technologies for personalized mental healthcare. Clinical research indicates that the onset and progression of Alzheimer’s disease lead to dementia and other mental health issues. As a result, the language capabilities of patient start to decline. In this paper, we show that machine learning-based unsupervised clustering of and anomaly detection with linguistic biomarkers are promising approaches for intuitive visualization and personalized early stage detection of Alzheimer’s disease. We demonstrate this approach on 10 year’s (1980 to 1989) of President Ronald Reagan’s speech data set. Key linguistic biomarkers that indicate early-stage AD are identified. Experimental results show that Reagan had early onset of Alzheimer’s sometime between 1983 and 1987. This finding is corroborated by prior work that analyzed his interviews using a statistical technique. The proposed technique also identifies the exact speeches that reflect linguistic biomarkers for early stage AD.
%R 10.18653/v1/2020.bionlp-1.14
%U https://aclanthology.org/2020.bionlp-1.14
%U https://doi.org/10.18653/v1/2020.bionlp-1.14
%P 133-139

Markdown (Informal)

[Personalized Early Stage Alzheimer’s Disease Detection: A Case Study of President Reagan’s Speeches](https://aclanthology.org/2020.bionlp-1.14) (Wang et al., BioNLP 2020)

• Personalized Early Stage Alzheimer’s Disease Detection: A Case Study of President Reagan’s Speeches (Wang et al., BioNLP 2020)

ACL

• Ning Wang, Fan Luo, Vishal Peddagangireddy, Koduvayur Subbalakshmi, and Rajarathnam Chandramouli. 2020. Personalized Early Stage Alzheimer’s Disease Detection: A Case Study of President Reagan’s Speeches. In Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing, pages 133–139, Online. Association for Computational Linguistics.