Abstract
Searching medical records is challenging due to their inherent implicit knowledge – such knowledge may be known by medical practitioners, but it is hidden from an information retrieval (IR) system. For example, it is intuitive for a medical practitioner to assert that patients with heart disease are likely to have records from the hospital’s cardiology department. Hence, we hypothesise that this implicit knowledge can be used to enhance a medical records search system that ranks patients based on the relevance of their medical records to a query. In this paper, we propose to group aggregates of medical records from individual hospital departments, which we refer to as department-level evidence, to capture some of the implicit knowledge. In particular, each department-level aggregate consists of all of the medical records created by a particular hospital department, which is then exploited to enhance retrieval effectiveness. Specifically, we propose two approaches to build the department-level evidence based on a federated search and a voting paradigm, respectively. In addition, we introduce an extended voting technique that could leverage this department-level evidence while ranking. We evaluate the retrieval effectiveness of our approaches in the context of the TREC 2011 Medical Records track. Our results show that modelling department-level evidence of records in medical records search improves retrieval effectiveness. In particular, our proposed approach to leverage department-level evidence built using a voting technique obtains results comparable to the best submitted TREC 2011 Medical Records track systems without requiring any external resources that are exploited in those systems.
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
Siegel, E., Channin, D.: Integrating the healthcare enterprise: A primer. RadioGraphics 21(5) (2001)
Tambouris, E., Willimas, M., Makropoulos, C.: Co-operative health information networks in Europe: experience from Greece and Scotland. Intl. J. Med. Inform. 64(1) (2000)
Hersh, W.: Health care information technology: progress and barriers. J. of the American Medical Association 292(18) (2004)
Hersh, W.: Information retrieval: A health and biomedical perspective, 3rd edn. Springer, New York (2009)
Voorhees, E., Tong, R.: Overview of the TREC 2011 Medical Records Track. In: Proc. of TREC (2011)
Jain, H., Thao, C., Huimin, Z.: Enhancing electronic medical record retrieval through semantic query expansion. Info. Systms. & E-Business Mngmnt. (2010)
Limsopatham, N., Macdonald, C., McCreadie, R., Ounis, I.: Exploiting Term Dependence while Handling Negation in Medical Search. In: Proc. of SIGIR (2012)
Callan, J.: Distributed Information Retrieval. Advances in Information Retrieval. Kluwer Academic Publisers (2000)
French, J., Powell, A., Callan, J., Viles, C., Emmitt, T., Prey, K., Mou, Y.: Comparing the performance of database selection algorithms. In: Proc. of SIGIR (1999)
Macdonald, C., Ounis, I.: Voting for candidates: adapting data fusion techniques for an expert search task. In: Proc. of CIKM (2006)
Robertson, S., Zaragoza, H., Taylor, M.: Simple BM25 extension to multiple weighted fields. In: Proc. of CIKM (2004)
Trotman, A.: Choosing document structure weights. IPM 41(2), 243–264 (2005)
Plachouras, V., Ounis, I.: Multinomial Randomness Models for Retrieval with Document Fields. In: Amati, G., Carpineto, C., Romano, G. (eds.) ECIR 2007. LNCS, vol. 4425, pp. 28–39. Springer, Heidelberg (2007)
Broder, A., Gabrilovich, E., Josifovski, V., Mavromatis, G., Metzler, D., Wang, J.: Exploiting site-level information to improve web search. In: Proc. of CIKM (2010)
Metzler, D., Novak, J., Cui, H., Reddy, S.: Building enriched document representations using aggregated anchor text. In: Proc. of SIGIR (2009)
Balog, K., Thomas, P., Craswell, N., Soboroff, I., Bailey, P.: Overview of the TREC 2008 Enterprise Track. In: Proc. of TREC (2008)
Balog, K., Azzopardi, L., de Rijke, M.: Formal models for expert finding in enterprise corpora. In: Proc. of SIGIR (2006)
Amati, G., Ambrosi, E., Bianchi, M., Gaibisso, C., Gambosi, G.: FUB, IASI-CNR and University of Tor Vergata at TREC 2007 Blog Track. In: Proc. of TREC (2007)
Ogilvie, P., Callan, J.: The effectiveness of query expansion for distributed information retrieval. In: Proc. of CIKM (2001)
Si, L., Callan, J.: Using sampled data and regression to merge search engine results. In: Proc. of SIGIR (2002)
Shokouhi, M.: Central-Rank-Based Collection Selection in Uncooperative Distributed Information Retrieval. In: Amati, G., Carpineto, C., Romano, G. (eds.) ECIR 2007. LNCS, vol. 4425, pp. 160–172. Springer, Heidelberg (2007)
Turtle, H., Croft, B.: Efficient probabilistic inference for text retrieval. In: Proc. of RIAO (1991)
Ounis, I., Amati, G., Plachouras, V., He, B., Macdonald, C., Lioma, C.: Terrier: A High Performance and Scalable Information Retrieval Platform. In: Proc. of OSIR at SIGIR (2006)
Limsopatham, N., Macdonald, C., Ounis, I., McDonald, G., Bouamrane, M.M.: University of Glasgow at Medical Records Track 2011: Experiments with Terrier. In: Proc. of TREC (2011)
Voorhees, E., Buckley, C.: The Effect of Topic Set Size on Retrieval Experiment Error. In: Proc. of SIGIR (2002)
Buckley, C., Voorhees, E.: Retrieval Evaluation with Incomplete Information. In: Proc. of SIGIR (2004)
Amati, G.: Probabilistic Models for Information Retrieval based on Divergence from Randomness. PhD thesis. University of Glasgow (2003)
Macdonald, C., Ounis, I.: Using Relevance Feedback in Expert Search. In: Amati, G., Carpineto, C., Romano, G. (eds.) ECIR 2007. LNCS, vol. 4425, pp. 431–443. Springer, Heidelberg (2007)
Blei, D., Ng, A., Jordan, M.: Latent dirichlet allocation. Journal of Machine Learning Research 3, 993–1022 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Limsopatham, N., Macdonald, C., Ounis, I. (2013). Aggregating Evidence from Hospital Departments to Improve Medical Records Search. In: Serdyukov, P., et al. Advances in Information Retrieval. ECIR 2013. Lecture Notes in Computer Science, vol 7814. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36973-5_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-36973-5_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36972-8
Online ISBN: 978-3-642-36973-5
eBook Packages: Computer ScienceComputer Science (R0)