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  <div class="vevent h-event"> <h1 class="summary p-name">pdxrlang meetup: Text mining & association rule mining in R to enhance public health surveillance</h1> <div class='date'><time class="dtstart dt-start" title="2017-02-13T18:30:00" datetime="2017-02-13T18:30:00">Monday, February 13, 2017 from 6:30</time>–<time class="dtend dt-end" title="2017-02-13T20:00:00" datetime="2017-02-13T20:00:00">8pm</time></div> <div class="location vcard p-location h-card"> <a class="url" href='http://calagator.org/venues/202395184'><span class='fn org p-name'>WeWork Custom House</span></a> <div class="adr p-adr h-adr"> <div class="street-address p-street-address">220 Northwest 8th Avenue</div> <span class="locality p-locality">Portland</span> , <span class="region p-region">OR</span> <span class="postal-code p-postal-code">97209</span> <div class="country-name p-country-name">US</div> (<a href='https://maps.google.com/maps?q=220%20Northwest%208th%20Avenue,%20Portland%20OR%2097209%20US'>map</a>) </div> </div> <div class="description p-description"> <p>Speaker: Matt Laidler</p> <p>Abstract: R can be a powerful tool for the process of systematically tracking health-related outcomes (i.e. surveillance) identified from administrative data sources. Tracking health outcomes in populations often relies on coded administrative data to standardize metrics and simplify processes. This may have the effect of overlooking useful information described in free text data, but due to data volume/size, may not be practical to process outside of text mining methods. This presentation will describe a use case of text mining and association rule mining in R for tracking health outcomes.</p> </div> <h3>Links</h3> <ul> <li><a class="url u-url" href="https://www.meetup.com/portland-r-user-group/events/236703908/">Website</a></li> </ul> </div>