<?xml version="1.0" encoding="UTF-8"?>
<xml>
<records>
<record>
  <contributors>
    <authors>
      <author>Lin, Wei-Chun</author>
    </authors>
    <secondary-authors>
      <author>Hribar, Michelle</author>
    </secondary-authors>
  </contributors>
  <titles>
    <title>Application of machine learning  for glaucoma surgical outcomes using EHR data</title>
    <translated-title/>
    <tertiary-title/>
  </titles>
  <periodical>
    <full-title/>
  </periodical>
  <alt-periodical>
    <full-title/>
    <abbr-1/>
  </alt-periodical>
  <pages/>
  <section/>
  <volume/>
  <number/>
  <keywords>
    <keyword>secondary use of ehr data</keyword>
    <keyword>Natural Language Processing</keyword>
    <keyword>Machine Learning</keyword>
    <keyword>Deep Learning</keyword>
    <keyword>Glaucoma</keyword>
    <keyword>Artificial Intelligence</keyword>
    <keyword>Ophthalmology</keyword>
    <keyword>Electronic Health Records</keyword>
  </keywords>
  <dates>
    <year>2022</year>
    <pub-dates>
      <date>2022</date>
    </pub-dates>
  </dates>
  <abstract>Glaucoma is a group of chronic and progressive eye diseases caused by damage to the optic nerve, which is usually related to increased intraocular pressure. In 2020, more than 80 million people were diagnosed with glaucoma, and the number is projected to increase to 110 million by 2040 worldwide. Glaucoma is currently the second leading cause of irreversible blindness worldwide and often results in long-term life quality impairment. Overall, our goal is to explore AI applications in ophthalmology and help with clinical care for glaucoma patients.</abstract>
  <pub-location/>
  <publisher>Oregon Health and Science University</publisher>
  <issn/>
  <isbn/>
  <custom3/>
  <custom7/>
  <notes/>
  <work-type>Dissertation</work-type>
  <electronic-resource-num>10.6083/vt150k29s</electronic-resource-num>
  <urls>
    <related-urls>
      <url>https://digitalcollections.ohsu.edu/record/9953/files/WeiChun.Lin.2022.pdf</url>
    </related-urls>
  </urls>
  <language/>
</record>

</records>
</xml>