Abstract
Diagnosis of diseases requires a large amount of discriminating diagnosis factors, including the risk factors, symptoms, and signs of the diseases, as well as the examinations and tests to detect the signs of the diseases. Relationships between individual diseases and the discriminating diagnosis factors may thus form a diagnosis knowledge map, which may even evolve when new medical findings are produced. However, manual construction and maintenance of a diagnosis knowledge map are both costly and difficult, and state-of-the-art text mining techniques have difficulties in identifying the diagnosis factors from medical texts. In this paper, we present a novel text mining technique PDFI (Proximity-based Diagnosis Factors Identifier) that improves various kinds of identification techniques by encoding term proximity contexts to them. Empirical evaluation is conducted on a broad range of diseases that have texts describing their symptoms and diagnosis in MedlinePlus, which aims at providing reliable and up-to-date healthcare information for diseases. The results show that PDFI significantly improves a state-of-the-art identifier in ranking candidate diagnosis factors for the diseases. The contribution is of practical significance in developing an intelligent system to provide disease diagnosis support to healthcare consumers and professionals.
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Liu, RL., Tung, SY., Lu, YL. (2011). Identifying Disease Diagnosis Factors by Proximity-Based Mining of Medical Texts. In: Nguyen, N.T., Kim, CG., Janiak, A. (eds) Intelligent Information and Database Systems. ACIIDS 2011. Lecture Notes in Computer Science(), vol 6592. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20042-7_18
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DOI: https://doi.org/10.1007/978-3-642-20042-7_18
Publisher Name: Springer, Berlin, Heidelberg
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