Computer-Based Coding of Occupation Codes for Epidemiological Analyses

Daniel E. Russ, Kwan-Yuet Ho, Calvin A. Johnson, Melissa C. Friesen
<span title="">2014</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="" style="color: black;">2014 IEEE 27th International Symposium on Computer-Based Medical Systems</a> </i> &nbsp;
Mapping job titles to standardized occupation classification (SOC) codes is an important step in evaluating changes in health risks over time as measured in inspection databases. However, manual SOC coding is cost prohibitive for very large studies. Computer based SOC coding systems can improve the efficiency of incorporating occupational risk factors into large-scale epidemiological studies. We present a novel method of mapping verbatim job titles to SOC codes using a large table of prior
more &raquo; ... edge available in the public domain that included detailed description of the tasks and activities and their synonyms relevant to each SOC code. Job titles are compared to our knowledge base to find the closest matching SOC code. A soft Jaccard index is used to measure the similarity between a previously unseen job title and the knowledge base. Additional information such as standardized industrial codes can be incorporated to improve the SOC code determination by providing additional context to break ties in matches.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.1109/cbms.2014.79</a> <a target="_blank" rel="external noopener" href="">pmid:25221787</a> <a target="_blank" rel="external noopener" href="">pmcid:PMC4161468</a> <a target="_blank" rel="external noopener" href="">dblp:conf/cbms/RussHJF14</a> <a target="_blank" rel="external noopener" href="">fatcat:56pnfkbtcvgubi64p3tuaph2ci</a> </span>
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