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    <mods:titleInfo>
        <mods:title>Mining and visualizing family history associations in the electronic health record: a case study for pediatric asthma</mods:title>
    </mods:titleInfo>
    <mods:name type="personal">
        <mods:namePart>Chen, Elizabeth S.</mods:namePart>
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            <mods:roleTerm type="text" authority="marcrelator">author</mods:roleTerm>
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    </mods:name>
    <mods:name type="personal">
        <mods:namePart>Melton, Genevieve B.</mods:namePart>
        <mods:role>
            <mods:roleTerm type="text" authority="marcrelator">author</mods:roleTerm>
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    </mods:name>
    <mods:name type="personal">
        <mods:namePart>Wasserman, Richard C.</mods:namePart>
        <mods:role>
            <mods:roleTerm type="text" authority="marcrelator">author</mods:roleTerm>
        </mods:role>
    </mods:name>
    <mods:name type="personal">
        <mods:namePart>Rosenau, Paul T.</mods:namePart>
        <mods:role>
            <mods:roleTerm type="text" authority="marcrelator">author</mods:roleTerm>
        </mods:role>
    </mods:name>
    <mods:name type="personal">
        <mods:namePart>Howard, Diantha B.</mods:namePart>
        <mods:role>
            <mods:roleTerm type="text" authority="marcrelator">author</mods:roleTerm>
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    </mods:name>
    <mods:name type="personal">
        <mods:namePart>Sarkar, Indra Neil</mods:namePart>
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            <mods:roleTerm type="text" authority="marcrelator">author</mods:roleTerm>
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    <mods:typeOfResource>text</mods:typeOfResource>
    <mods:genre authority="aat">papers (documents)</mods:genre>
    <mods:originInfo>
        <mods:dateCreated keyDate="yes" encoding="w3cdtf">2015</mods:dateCreated>
    </mods:originInfo>
    <mods:language>
        <mods:languageTerm type="code" authority="iso639-2b">eng</mods:languageTerm>
    </mods:language>
    <mods:note displayLabel="Source">Electronic Health Record</mods:note>
    <mods:note displayLabel="Method">Data Mining</mods:note>
    <mods:note displayLabel="Condition">Asthma</mods:note>
    <mods:abstract>Asthma is the most common chronic childhood disease and has seen increasing prevalence worldwide. While there is existing evidence of familial and other risk factors for pediatric asthma, there is a need for further studies to explore and understand interactions among these risk factors. The goal of this study was to develop an approach for mining, visualizing and evaluating association rules representing pairwise interactions among potential familial risk factors based on information documented as part of a patient's family history in the electronic health record. As a case study, 10,260 structured family history entries for a cohort of 1,531 pediatric asthma patients were extracted and analyzed to generate family history associations at different levels of granularity. The preliminary results highlight the potential of this approach for validating known knowledge and suggesting opportunities for further investigation that may contribute to improving prediction of asthma risk in children.</mods:abstract>
    <mods:note type="funding">This research was supported in part by the National Library of Medicine of the National Institutes of Health under award number R01LM011364</mods:note>
    <mods:note>Paper presented at the 2015 AMIA Annual Symposium</mods:note>

    <mods:subject authority="local">
        <mods:topic>Family history</mods:topic>
    </mods:subject>


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        <mods:titleInfo>
            <mods:title>AMIA Annual Symposium Proceedings</mods:title>

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        <mods:originInfo>
            <mods:dateCreated>2015</mods:dateCreated>
            <mods:dateCreated keyDate="yes" encoding="w3cdtf">2015</mods:dateCreated>
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        <mods:physicalDescription>
            <mods:extent>p. 396-405</mods:extent>
        </mods:physicalDescription>
        <mods:identifier type="PubMed Central ID">PMCID: PMC4765567</mods:identifier>


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        Creative Commons Attribution-NonCommercial 4.0 International License</mods:accessCondition>
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