Abstract
Patient demographics are of great importance in clinical decision processes for both diagnosis, tests and treatments. Natural language is the standard in clinical case reports, however, numerical concepts, such as age, do not show their full potential when treated as text tokens. In this paper, we consider the patient age as a numerical dimension and investigate several Kernel methods to smooth a temporal retrieval model. We extract patient age from the clinical case narrative and extend a Dirichlet language to include the temporal dimension. Experimental results on a clinical decision support task, showed that our proposal achieves a relative improvement of 5.7% at the top 10 retrieved documents over a time agnostic baseline.
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Notes
- 1.
Where \(\mathbf {1}\) represents the indicator function.
References
Balaneshinkordan, S., Kotov, A., Xisto, R.: WSU-IR at TREC 2015 CDS track: joint weighting of explicit and latent medical query concepts from diverse sources. In: TREC (2015)
Demner-Fushman, D., Chapman, W.W., McDonald, C.J.: What can natural language processing do for clinical decision support? J. Biomed. Inform. 42(5), 760–772 (2009)
Drosatos, G., Roumeliotis, S., Kaldoudi, E., Arampatzis, A.: DUTH at TREC 2015 clinical decision support track. In: TREC (2015)
Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S.J., McClosky, D.: The Stanford CoreNLP natural language processing toolkit. In: ACL System Demonstrations (2014)
Ponte, J.M., Croft, W.B.: A language modeling approach to information retrieval. In: ACM SIGIR (1998)
Roberts, K., Simpson, M.S., Voorhees, E.M., Hersh, W.R.: Overview of the TREC 2015 clinical decision support track. In: TREC (2015)
Savova, G.K., Masanz, J.J., Ogren, P.V., et al.: Mayo clinical text analysis and knowledge extraction system (cTAKES): architecture, component evaluation and applications. J. Am. Med. Inform. Assoc. 17(5), 507–513 (2010)
Sun, W., Rumshisky, A., Uzuner, O.: Evaluating temporal relations in clinical text: 2012 i2b2 challenge. J. Am. Med. Inform. Assoc. 20(5), 806–813 (2013)
Zhai, C., Lafferty, J.: A study of smoothing methods for language models applied to ad hoc information retrieval. In: ACM SIGIR (2001)
Acknowledgements
This work has been partially funded by the NOVA LINCS project ref. UID/CEC/04516/2013.
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Ramalho, R., Mourão, A., Magalhães, J. (2018). Patient-Age Extraction for Clinical Reports Retrieval. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds) Advances in Information Retrieval. ECIR 2018. Lecture Notes in Computer Science(), vol 10772. Springer, Cham. https://doi.org/10.1007/978-3-319-76941-7_46
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DOI: https://doi.org/10.1007/978-3-319-76941-7_46
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