Abstract
The UMLS semantic types and natural language processing techniques were collectively utilized to extract PICO elements from the titles and abstracts of 114 MEDLINE articles. 24 sets of PICO elements were generated from the articles based on the derivation of, and the tokenization methods and weighting schemes applied to the elements. The similarity of the I and C elements (called jointly the “Interventions”) between pairs of documents was calculated using 42 similarity/distance measures. Similar interventions were grouped together using complete-/average-/ward-link hierarchical clustering. The similarity measure, Yule, performed significantly better than other measures in identifying paired interventions derived from the titles and which had been pre-processed into single term and weighted by binary term-occurrence. The clustering algorithm, complete-link, provides the most appropriate structure for the visualization of interventions. Similarity-based clustering gave a higher mean average precision than random-baseline clustering (MAP = 0.4298 vs. 0.2364) over the 25 queries evaluated.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Ely, J.W., Osheroff, J.A., Chambliss, M.L., Ebell, M.H., Rosenbaum, M.E.: Answering physicians’ clinical questions: obstacles and potential solutions. J. Am. Med. Inform. Assoc. 12(2), 217–224 (2005)
Huang, X., Lin, J., Demner-Fushman, D.: Evaluation of PICO as a knowledge representation for clinical questions. In: AMIA Annual Symposium Proceedings 2006, pp. 359–363 (2006)
Cao, Y., Liu, F., Simpson, P., Antineau, L., Bennet, A., Cimino, J.J., Ely, J., Yu, H.: AskHERMES: an online question answering system for complex clinical questions. J. Biomed. Inform. 44(2), 227–288 (2011)
Bergus, G.R., Randall, C.S., Sinift, S.D., Rosenthal, D.M.: Does the structure of clinical questions affect the outcome of curbside consultations with specialty colleagues? Arch. Fam. Med. 9(6), 541–547 (2000)
Demner-Fushman, D., Lin, J.: Answering clinical questions with knowledge-based and statistical techniques. Association for Computational Linguistics 33(1), 63–103 (2007)
Boudin, F., Nie, J.Y., Bartlett, J.C., Grad, R., Pluye, P., Dawes, M.: Combining classifiers for robust PICO element detection. BMC Med. Inform. Decis. Mak. 10(1), 29–36 (2010)
Aronson, A.R.: Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. In: Proceedings of AMIA Annual Symposium 2001, pp. 17–21 (2001)
RapidMiner: Report the future, http://rapid-i.com/content/blogcategory/38/69/ (Assessed: August 2013)
R: The R project for statistical computing, http://www.r-project.org (Assessed: August 2013)
Trec_eval, http://trec.nist.gov/trec_eval/ (Assessed: August 2013)
Smith, R.: What clinical information do doctors need? BMJ 313(7064), 1062–1068 (1996)
Davies, K., Harrison, J.: The information-seeking behavior of doctors: a review of the evidence. Health Info. Libr. J. 24(2), 78–94 (2007)
Schwartz, K., Northrup, J., Crowell, K., Lauder, N., Neale, A.V.: Use of on-line evidence-based resources at the point of care. Family Medicine 35(4), 251–256 (2003)
Yu, H., Cao, Y.G.: Automatically extracting information needs from ad hoc clinical questions. In: AMIA Annual Symposium Proceedings 2008, pp. 96–100 (2008)
Davies, K.: UK doctors awareness and use of specified electronic evidence-based medicine resources. Inform. Health Soc. Care 36(1), 1–19 (2011)
Schilling, L.M., Steiner, J.F., Lundahl, K., Anderson, R.J.: Residents’ patient-specific clinical questions: opportunities for evidence-based learning. Academic Medicine 80(1), 51–56 (2005)
Ely, J.W., Osheroff, J.A., Maviglia, S.M., Rosenbaum, M.E.: Patient-care questions that physicians are unable to answer. J. Am. Med. Inform. Assoc. 14(4), 407–414 (2007)
Schardt, C., Adams, M.B., Owens, T., Keitz, S., Fontelo, P.: Utilization of the PICO framework to improve searching PubMed for clinical questions. BMC Med. Inform. Decis. Mak. 7(1), 16 (2007)
Rios, L.P., Ye, C., Thabane, L.: Association between framing of the research question using the PICOT format and reporting quality of randomized controlled trials. BMC Med. Res. Methodol. 10(1), 11–18 (2010)
Robinson, K.A., Saldanha, I.J., Mckoy, N.A.: Frameworks for determining research gaps during systematic reviews. In: Methods Future Research Needs Reports, vol. 2. Agency for Healthcare Research and Quality, Rockville (MD) (2011)
Schlosser, R.W., Koul, R., Costello, J.: Asking well-built questions for evidence-based practice in augmentative and alternative communication. Journal of Communication Disorders 40(3), 225–238 (2007)
Staunton, M.: Evidence-based radiology: steps 1 and 2 – asking answerable questions and searching for evidence. Radiology 242(1), 23–31 (2007)
Athenikos, S.J., Han, H.: Biomedical question answering: a survey. Comput. Methods Programs Biomed. 99(1), 1–24 (2010)
Demner-Fushman, D., Seckman, C., Fisher, C., Hauser, S.E., Clayton, J., Thoma, G.R.: A prototype system to support evidence-based practice. In: AMIA Annual Symposium Proceedings 2008, pp. 151–155 (2008)
Niu, Y., Hirst, G., McArthur, G., Rodriguez-Gianolli, P.: Answering clinical questions with role identification. In: Proceedings, Workshop on Natural Language Processing in Biomedicine, 41st Annual Meeting of the Association for Computational Linguistics, pp. 73–80 (2003)
Yu, H., Kaufman, D.: A cognitive evaluation of four online search engines for answering definitional questions posed by physicians. In: Pacific Symposium on Biocomputing 2007, pp. 328–339 (2007)
Cao, Y.G., Cimino, J.J., Ely, J., Yu, H.: Automatically extracting information needs from complex clinical questions. J. Biomed. Inform. 43(6), 962–971 (2010)
Muresan, G., Harper, D.J.: Topic modelling for mediated access to very large document collections. J. Am. Soc. Inf. Sci. Technol. 55(10), 892–910 (2004)
Subhashini, R., Kumar, V.: Evaluating the performance of similarity measures used in document clustering and information retrieval. In: Proceedings of the First International Conference on Integrated Intelligent Computing 2010, pp. 27–31 (2010)
Rangrej, A., Kulkarni, S., Tendulkar, A.: Comparative study of clustering techniques for short text documents. In: Proceedings of the 20th International Conference Comparison on World Wide Web 2011, pp. 111–112 (2011)
Malik, H.H., Kender, J.R.: High quality, efficient hierarchical document clustering using closed interesting itemsets. In: Proceedings of the Sixth International Conference on Data Mining 2006, pp. 991–996 (2006)
Aljaber, B., Stokes, N., Bailey, J., Pei, J.: Document clustering of scientific texts using citation contexts. Information Retrieval 13(2), 101–131 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Vong, WT., Then, P.H.H. (2014). Visualization of PICO Elements for Information Needs Clarification and Query Refinement. In: Tseng, V.S., Ho, T.B., Zhou, ZH., Chen, A.L.P., Kao, HY. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8444. Springer, Cham. https://doi.org/10.1007/978-3-319-06605-9_30
Download citation
DOI: https://doi.org/10.1007/978-3-319-06605-9_30
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-06604-2
Online ISBN: 978-3-319-06605-9
eBook Packages: Computer ScienceComputer Science (R0)