<mods:mods xmlns:mods="http://www.loc.gov/mods/v3" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" ID="bdr698107" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/mods/v3/mods-3-6.xsd">
   <mods:titleInfo>
      <mods:title>Using Machine Learning to Predict Surgical Site Infection</mods:title>
   </mods:titleInfo>
   <mods:name type="personal">
      <mods:namePart>Biron, Dustin</mods:namePart>
      <mods:role>
         <mods:roleTerm type="text">creator</mods:roleTerm>
      </mods:role>
   </mods:name>
   <mods:name type="personal">
      <mods:namePart>Jain, Sukrit</mods:namePart>
      <mods:role>
         <mods:roleTerm type="text">creator</mods:roleTerm>
      </mods:role>
   </mods:name>
   <mods:name type="personal">
      <mods:namePart>Stey, Paul</mods:namePart>
      <mods:role>
         <mods:roleTerm type="text">creator</mods:roleTerm>
      </mods:role>
   </mods:name>
   <mods:name type="personal">
      <mods:namePart>Anand, Rajsavi</mods:namePart>
      <mods:role>
         <mods:roleTerm type="text">creator</mods:roleTerm>
      </mods:role>
   </mods:name>
   <mods:name type="personal">
      <mods:namePart>Chen, Elizabeth S.</mods:namePart>
      <mods:role>
         <mods:roleTerm type="text">creator</mods:roleTerm>
      </mods:role>
   </mods:name>
   <mods:name type="personal">
      <mods:namePart>Sarkar, Indra Neil</mods:namePart>
      <mods:role>
         <mods:roleTerm type="text">creator</mods:roleTerm>
      </mods:role>
   </mods:name>
   <mods:typeOfResource>text</mods:typeOfResource>
   <mods:genre authority="aat">posters</mods:genre>
   <mods:originInfo>
      <mods:dateCreated keyDate="yes" encoding="w3cdtf">2017</mods:dateCreated>
   </mods:originInfo>
   <mods:language>
      <mods:languageTerm type="code" authority="iso639-2b">eng</mods:languageTerm>
   </mods:language>
   <mods:note displayLabel="Scholarly concentration">Biomedical Informatics</mods:note>
   <mods:note>All rights reserved</mods:note>
   <mods:abstract>Surgical site infection (SSI) is a rare, but serious complication for patients undergoing total joint replacements. We aimed to create a machine learning algorithm that could accurately diagnose infection in the 199 patients that had undergone total knee or hip replacements from the MIMIC-III database.1 After data preprocessing, processing, and analysis, we determined that the dataset was too small to produce a meaningful machine learning algorithm. However, we recognize that this study serves as an important framework for studying larger datasets with more patients.</mods:abstract>
   <mods:subject authority="lcsh">
      <mods:topic>Machine learning</mods:topic>
   </mods:subject>
   <mods:subject authority="local">
      <mods:topic>Risk prediction</mods:topic>
   </mods:subject>

</mods:mods>