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Improving Medical Information Retrieval with PICO Element Detection

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Advances in Information Retrieval (ECIR 2010)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5993))

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Abstract

Without a well formulated and structured question, it can be very difficult and time consuming for physicians to identify appropriate resources and search for the best available evidence for medical treatment in evidence-based medicine (EBM). In EBM, clinical studies and questions involve four aspects: Population/Problem (P), Intervention (I), Comparison (C) and Outcome (O), which are known as PICO elements. It is intuitively more advantageous to use these elements in Information Retrieval (IR). In this paper, we first propose an approach to automatically identify the PICO elements in documents and queries. We test several possible approaches to use the identified elements in IR. Experiments show that it is a challenging task to determine accurately PICO elements. However, even with noisy tagging results, we can still take advantage of some PICO elements, namely I and P elements, to enhance the retrieval process, and this allows us to obtain significantly better retrieval effectiveness than the state-of-the-art methods.

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References

  1. Andrenucci, A.: Automated Question-Answering Techniques and the Medical Domain. In: HEALTHINF, pp. 207–212 (2008)

    Google Scholar 

  2. Aronson, A.R.: Effective Mapping of Biomedical Text to the UMLS Metathesaurus: The MetaMap Program. In: AMIA Symposium (2001)

    Google Scholar 

  3. Bai, J., Nie, J.Y., Paradis, F.: Using language models for text classification. In: Asia Information Retrieval Symposium (AIRS), Beijing, China (2004)

    Google Scholar 

  4. Chung, G.: Sentence retrieval for abstracts of randomized controlled trials. BMC Medical Informatics and Decision Making 9(1), 10 (2009)

    Article  Google Scholar 

  5. Demner-Fushman, D., Lin, J.: Answering clinical questions with knowledge-based and statistical techniques. Computational Linguistics 33(1), 63–103 (2007)

    Article  Google Scholar 

  6. Hansen, M.J., Rasmussen, N.O., Chung, G.: A method of extracting the number of trial participants from abstracts describing randomized controlled trials. Journal of Telemedicine and Telecare 14(7), 354–358 (2008)

    Article  Google Scholar 

  7. Hersh, W.R.: Information retrieval: a health and biomedical perspective. Springer, Heidelberg (2008)

    Google Scholar 

  8. McKnight, L., Srinivasan, P.: Categorization of Sentence Types in Medical Abstracts. In: AMIA Symposium (2003)

    Google Scholar 

  9. Rindflesch, T.C., Fiszman, M.: The interaction of domain knowledge and linguistic structure in natural language processing: interpreting hypernymic propositions in biomedical text. Journal of Biomedical Informatics 36(6), 462–477 (2003)

    Article  Google Scholar 

  10. Schardt, C., Adams, M., Owens, T., Keitz, S., Fontelo, P.: Utilization of the PICO framework to improve searching PubMed for clinical questions. BMC Medical Informatics and Decision Making 7(1), 16 (2007)

    Article  Google Scholar 

  11. Weinfeld, J.M., Finkelstein, K.: How to answer your clinical questions more efficiently. Family practice management 12(7), 37 (2005)

    Google Scholar 

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Boudin, F., Shi, L., Nie, JY. (2010). Improving Medical Information Retrieval with PICO Element Detection. In: Gurrin, C., et al. Advances in Information Retrieval. ECIR 2010. Lecture Notes in Computer Science, vol 5993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12275-0_8

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  • DOI: https://doi.org/10.1007/978-3-642-12275-0_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12274-3

  • Online ISBN: 978-3-642-12275-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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