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<Paper uid="W02-0310">
  <Title>Analyzing the Semantics of Patient Data to Rank Records of Literature Retrieval</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
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6 Discussion
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    <Paragraph position="0"> Our main goal in this project was to assess the effect of the use of clinical data to improve presentation of medical literature. We evaluated three semantic methods.</Paragraph>
    <Paragraph position="1"> The level of association between pairs of subjects ranged from -0.07 to 0.52. The level of association associations among physicians seemed to be similar to levels of agreement between 2 independent raters reported in the literature.(Wilczynski, McKibbon, and Haynes, 2001) No single physician stood out as significantly different from the others.</Paragraph>
    <Paragraph position="2"> The graph matching algorithm highly correlated with physicians' average, although it did not perform as well as individual physicians. This finding encourages the use of clinical data to determine the relevance of medical literature to the care of individual patients. In an integrated system (medical record with information resources) this positive correlation suggests that our method can facilitate presentation of online biomedical literature. For instance, if the electronic medical record is integrated to an existent information retrieval, findings from an individual medical record can be used to rearrange the way retrieved information is presented; in a way that literature matching that individual's medical record will be presented first, rather than the usual presentation in reverse chronological order.</Paragraph>
    <Paragraph position="3"> The combined method also correlated significantly with physicians' average, although its performance was not as good as of the simple graph matching. This result may be due to a negative effect of the associations in the knowledge over the matching. There was no correlation between the methods that use the co-occurrence of semantic types in medical literature citations and the average of physicians. The automated method based on the chronological order of articles did not correlate with physicians' average.</Paragraph>
    <Paragraph position="4"> The poor results of the method which used the knowledge base of semantic co-occurrences in Medline citations may be due to several aspects. The terms used for indexing medical citations may not correspond well to data usually found in medical records. Approaches using the UMLS Semantic Net may be also somewhat limited by the fact approximately one fourth of the Metathesaurus concepts are assigned several semantic types, which makes it difficult to get a precise understanding of the cooccurrences.(Burgun and Bodenreider, 2001) We believe enhancements can still be made.</Paragraph>
    <Paragraph position="5"> The graph matching algorithm is highly dependent on the output of the natural language processor. The general language processor used to parse both clinical data and citations was never validated for this use. AQUA was designed to translate user's natural language queries into a conceptual graph representation. It was developed on a corpus of clinical queries.</Paragraph>
    <Paragraph position="6"> Prior to this study, the parser was trained with only a few sentences from the medical literature. The complexity of the clinical data and medical literature involved in the study generated a significant number of &amp;quot;broken&amp;quot; graphs. The similarity found between the graphs was usually at the level of single nodes. It was also observed that the parser had difficult with very long sentences and sentences in the results section of the abstract. An example of a sentence partially parsed is &amp;quot;Furthermore, patients treated with aprotinin had significantly less total postoperative blood loss (718 +/- 340 ml vs 920 +/- 387 ml, p =0.04)&amp;quot;. With enhancements to the natural language processor, we believe we could obtain a better representation of the data, and consequently more accurate results.</Paragraph>
    <Paragraph position="7"> The use of UMLS Semantic Net may have also contributed to the elevated incidence of &amp;quot;broken&amp;quot; graphs. Mendonca and Cimino (Mendonca and Cimino, 2001) found that only 22.99% of the associations of semantic types based on MeSH terms retrieved from the medical literature had a direct semantic relationship in the UMLS Semantic Net. A careful appreciation of the missing relationships may help us to understand whether the addition of new semantic relationships can contribute to a better representation of clinical and literature data.</Paragraph>
    <Paragraph position="8"> Whether improvements in the parser to allow it to handle medical literature and complex clinical data would improve the performance of the automated methods is unclear; further studies are needed. The use of this method in association with other information retrieval techniques is being investigated by the authors.</Paragraph>
  </Section>
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