<rdf:RDF xmlns:rdf="http://www.openarchives.org/OAI/2.0/rdf/" xmlns:ow="http://www.ontoweb.org/ontology/1#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:ds="http://dspace.org/ds/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/rdf/ http://www.openarchives.org/OAI/2.0/rdf.xsd">
   <ow:Publication rdf:about="oai:digibug.ugr.es:10481/80079">
      <dc:title>A Textual Data-Oriented Method for Doctor Selection in Online Health Communities</dc:title>
      <dc:creator>Du, Yinfeng</dc:creator>
      <dc:creator>Morente Molinera, Juan Antonio</dc:creator>
      <dc:creator>Herrera Viedma, Enrique</dc:creator>
      <dc:subject>Online health communities</dc:subject>
      <dc:subject>Doctor selection</dc:subject>
      <dc:subject>Patient satisfactions</dc:subject>
      <dc:subject>Improved MULTIMOORA</dc:subject>
      <dc:subject>Selection criteria</dc:subject>
      <dc:description>As doctor–patient interactive platforms, online health communities (OHCs) offer patients&#xd;
massive information including doctor basic information and online patient reviews. However, how to&#xd;
develop a systematic framework for doctor selection in OHCs according to doctor basic information&#xd;
and online patient reviews is a challenged issue, which will be explored in this study. For doctor&#xd;
basic information, we define the quantification method and aggregate them to characterize relative&#xd;
influence of doctors. For online patient reviews, data analysis techniques (i.e., topics extraction and&#xd;
sentiment analysis) are used to mine the core attributes and evaluations. Subsequently, frequency&#xd;
weights and position weights are respectively determined by a frequency-oriented formula and&#xd;
a position score-based formula, which are integrated to obtain the final importance of attributes.&#xd;
Probabilistic linguistic-prospect theory-multiplicative multiobjective optimization by ratio analysis&#xd;
(PL-PT-MULTIMOORA) is proposed to analyze patient satisfactions on doctors. Finally, selection&#xd;
rules are made according to doctor influence and patient satisfactions so as to choose optimal and&#xd;
suboptimal doctors for rational or emotional patients. The designed textual data-driven method is&#xd;
successfully applied to analyze doctors from Haodf.com and some suggestions are given to help&#xd;
patients pick out optimal and suboptimal doctors.</dc:description>
      <dc:date>2023-02-20T11:46:07Z</dc:date>
      <dc:date>2023-02-20T11:46:07Z</dc:date>
      <dc:date>2023-01-09</dc:date>
      <dc:type>journal article</dc:type>
      <dc:identifier>Du, Y... [et al.]. A Textual Data-Oriented Method for Doctor Selection in Online Health Communities. Sustainability 2023, 15, 1241. [https://doi.org/10.3390/su15021241]</dc:identifier>
      <dc:identifier>https://hdl.handle.net/10481/80079</dc:identifier>
      <dc:identifier>10.3390/su15021241</dc:identifier>
      <dc:language>eng</dc:language>
      <dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>
      <dc:rights>open access</dc:rights>
      <dc:rights>Atribución 4.0 Internacional</dc:rights>
      <dc:publisher>MDPI</dc:publisher>
   </ow:Publication>
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