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        <article-title>Applying Information Retrieval to the Electronic Health Record for Cohort Discovery and Rare Disease Detection</article-title>
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      <contrib-group>
        <contrib contrib-type="author">
          <string-name>Department of Medical Informatics</string-name>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Clinical Epidemiology</string-name>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Portland</string-name>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <aff id="aff0">
          <label>0</label>
          <institution>Oregon Health &amp; Science University</institution>
          ,
          <country country="US">USA</country>
        </aff>
      </contrib-group>
      <abstract>
        <p>The last decade has seen rapid adoption of electronic health records in the United States and elsewhere. This has resulted in vast amounts of data that can be re-used for other purposes such as clinical research. However, most of this data is non-standardized and unstructured, making retrieval and other uses challenging. This</p>
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    <sec id="sec-1">
      <title>William</title>
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    <sec id="sec-2">
      <title>Hersh</title>
      <p>talk</p>
      <p>will describe recent research applying and evaluating
information retrieval techniques to two use cases: discovering
cohorts for clinical research studies and detecting rare diseases.
Permission to make digital or hard copies of part or all of this work for personal or
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be honored. For all other uses, contact the owner/author(s).</p>
      <p>HSDM’20, February, 2020, Houston, Texas USA
Copyright © 2019 for this paper by its author. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).</p>
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