Abstract
Information and Communication Technologies has led to a biomedical data explosion. A proportional growth has been produced regarding the amount of scientific literature, but information retrieval methods did not follow the same pattern. By using specialized clinical search engines such as PubMed, Medscape and Cochrane, biomedical publications has became instantly available for clinical users. However, additional parameters, such as user context, are not taken into account yet. Initial queries still retrieve too many results without a relevance-based ranking. The objective of this work was to develop a new method to enhance scientific literature searches from various sources, by including patient information in the retrieval process. Two pathologies have been used to test the proposed method: diabetes and arterial hypertension. Results obtained suggest the suitability of the approach, highlighting the publications related to patient characteristics.
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References
Tratamiento 2.0, http://www.tratamiento20.com
Healthcare Information and Management Systems Society: Consensus definition of an Electronic Health Record, http://www.himss.org/ASP/topics_ehr.asp
Weaver, C.A., Warren, J.J., Delaney, C., et al.: Bedside, Classroom and Bench: Collaborative Strategies to Generate Evidence-Based Knowledge for Nursing Practice. Int. J. Med. Inform. 74, 989–999 (2005)
Antolik, J.: Automatic Annotation of Medical Records. Stud. Health Technol. Inform. 116, 817–822 (2005)
Natarajan, K., Stein, D., Jain, S., et al.: An Analysis of Clinical Queries in an Electronic Health Record Search Utility. Int. J. Med. Inform. (2010)
Keen, P.G.W., Morton, M.S.S.: Decision support systems: An organizational perspective. Addison Wesley Publishing Company (1978)
Kawamoto, K., Houlihan, C.A., Balas, E.A., et al.: Improving Clinical Practice using Clinical Decision Support Systems: A Systematic Review of Trials to Identify Features Critical to Success. BMJ 330, 765 (2005)
Sim, I., Gorman, P., Greenes, R.A., et al.: Clinical Decision Support Systems for the Practice of Evidence-Based Medicine. J. Am. Med. Inform. Assoc. 8, 527–534 (2001)
van der Weijden, T., Legare, F., Boivin, A., et al.: How to Integrate Individual Patient Values and Preferences in Clinical Practice Guidelines? A Research Protocol. Implement Sci. 5, 10 (2010)
Seidling, H.M., Schmitt, S.P., Bruckner, T., et al.: Patient-Specific Electronic Decision Support Reduces Prescription of Excessive Doses. Qual. Saf. Health. Care (2010)
Evidence-Based Medicine Working Group: Evidence-Based Medicine. A New Approach to Teaching the Practice of Medicine. JAMA 268, 2420–2425 (1992)
Huang, X., Lin, J., Demner-Fushman, D.: Evaluation of PICO as a Knowledge Representation for Clinical Questions. In: AMIA. Annu. Symp. Proc., pp. 359–363 (2006)
Couto, F.M., Silva, M.J., Lee, V., et al.: GOAnnotator: Linking Protein GO Annotations to Evidence Text. J. Biomed. Discov. Collab. 1, 19 (2006)
Atserias, J., Casas, B., Comelles, E., et al.: FreeLing 1.3: Syntactic and Semantic Services in an Open-Source NLP Library. In: Proceedings of the 5th International Conference on Language Resources and Evaluation (LREC 2006), pp. 48–55 (2006)
Cunningham, H.: GATE, a General Architecture for Text Engineering. Computers and the Humanities 36, 223–254 (2002)
Díaz-Galiano, M.C., García-Cumbreras, M.Á., Martín-Valdivia, M.T., Montejo-Ráez, A., Ureña-López, L.A.: Integrating MeSH Ontology to Improve Medical Information Retrieval. In: Peters, C., Jijkoun, V., Mandl, T., Müller, H., Oard, D.W., Peñas, A., Petras, V., Santos, D. (eds.) CLEF 2007. LNCS, vol. 5152, pp. 601–606. Springer, Heidelberg (2008)
Spackman, K.A., Reynoso, G.: Examining SNOMED from the Perspective of Formal Ontological Principles: Some Preliminary Analysis and Observations. KR-MED., 72–80 (2004)
Jacso, P.: Thoughts about Federated Searching. Information Today 21, 17 (2004)
Lee, K.F.: Automatic speech recognition: The development of the SPHINX system. Kluwer Academic Pub. (1989)
Karopka, T., Scheel, T., Bansemer, S., et al.: Automatic Construction of Gene Relation Networks using Text Mining and Gene Expression Data. Med. Inform. Internet Med. 29, 169–183 (2004)
Srinivasan, P.: MeSHmap: A Text Mining Tool for MEDLINE. In: Proc. AMIA. Symp., pp. 642–646 (2001)
Kankar, P., Adak, S., Sarkar, A., et al.: MedMeSH Summarizer: Text Mining for Gene Clusters, pp. 11–13 (2002)
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Jimenez-Castellanos, A. et al. (2013). Biomedical Literature Retrieval Based on Patient Information. In: Fred, A., Filipe, J., Gamboa, H. (eds) Biomedical Engineering Systems and Technologies. BIOSTEC 2011. Communications in Computer and Information Science, vol 273. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29752-6_23
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DOI: https://doi.org/10.1007/978-3-642-29752-6_23
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