Abstract
The goal of Clinical Decision Support (CDS) is to help physicians find useful information from a collection of medical articles with respect to the given patient records, in order to take the best care of their patients. Most of the existing CDS methods do not sufficiently consider the semantic evidence, hence the potential in improving the performance in biomedical articles retrieval. This paper proposes a novel feedback-based approach which considers the semantic association between a retrieved biomedical article and a pseudo feedback set. Evaluation results show that our method outperforms the strong baselines, and is able to improve over the best runs in the CDS tasks of TREC 2014 & 2015.
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
Notes
- 1.
- 2.
- 3.
The learned embeddings of words and biomedical articles can be downloaded from http://gucasir.org/CDS.tgz.
References
Abacha, A., Khelifi, S.: LIST at TREC 2015 clinical decision support track: question analysis and unsupervised result fusion. In: TREC (2015)
Dai, A., Olah, C., Le, Q.: Document embedding with paragraph vectors. CoRR, abs/1507.07998 (2015)
Balaneshinkordan, S., Kotov, A., Xisto, R.: WSU-IR at TREC 2015 clinical decision support track: joint weighting of explicit and latent medical query concepts from diverse sources. In: TREC (2015)
Bengio, Y., Schwenk, H., Senécal, J., Morin, F., Gauvain, J.: Neural probabilistic language models. In: Holmes, D.E., Jain, L.C. (eds.) STUDFUZZ, vol. 194, pp. 137–186. Springer, Heidelberg (2006). doi:10.1007/3-540-33486-6_6
Bergstra, J., Bardenet, R., Kégl, B., Bengio, Y.: Algorithms for hyper-parameter optimization. In: Advances in Neural Information Processing Systems, pp. 2546–2554 (2011)
Blei, D., Ng, A., Jordan, M.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Choi, S., Choi, J.: SNUMedinfo at TREC CDS Track 2014: Medical Case-based Retrieval Task. Technical report, DTIC Document (2014)
Chowdhury, G.: TREC: experiment and evaluation in information retrieval. Online Inf. Rev. 5 (2007)
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011)
Cummins, R.: Clinical decision support with the SPUD language model. In: TREC (2015
Cummins, R., Paik, J., Lv, Y.: A pólya urn document language model for improved information retrieval. ACM Trans. Inf. Syst. (TOIS) 33(4), 21 (2015)
Goldberg, Y., Levy, O.: word2vec explained: deriving Mikolov et al’.s negative-sampling word-embedding method. CoRR, abs/1402.3722 (2014)
Goodwin, T., Harabagiu, S.: UTD at TREC 2014: query expansion for clinical decision support. Technical report, DTIC Document (2014)
Le, Q., Mikolov, T.: Distributed representations of sentences and documents. CoRR, abs/1405.4053 (2014)
Manning, C., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, New York (2008)
Metzler, D., Croft, W.: A Markov random field model for term dependencies. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 472–479. ACM (2005)
Mikolov, T., Yih, W., Zweig, G.: Linguistic regularities in continuous space word representations. In: HLT-NAACL (2013)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. CoRR, abs/1301.3781 (2013)
Mnih, A., Hinton, G.: A scalable hierarchical distributed language model. In: Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, pp. 1081–1088, December 2008
Palotti, J., Hanbury, A.: TUW @ TREC clinical decision support track 2015. In: TREC (2015)
Ponte, J., Croft, W.: A language modeling approach to information retrieval. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 275–281. ACM (1998)
Roberts, K., Simpson, M., Voorhees, E., Hersh, W.: Overview of the TREC 2015 clinical decision support track. In: TREC (2015)
Robertson, S., Walker, S., Beaulieu, M., Gatford, M., Payne, A.: Okapi at TREC-4. In: TREC, pp. 73–96 (1996)
Simpson, M., Voorhees, E., Hersh, W.: Overview of the TREC 2014 Clinical Decision Support Track. Technical report, DTIC Document (2014)
Song, Y., He, Y., Hu, Q., He, L.: ECNU at 2015 CDS track: two re-ranking methods in medical information retrieval. In: TREC (2015)
Turian, J., Ratinov, L., Bengio, Y.: Word representations: a simple and general method for semi-supervised learning. In: Proceedings of the 48th annual meeting of the association for computational linguistics, pp. 384–394. Association for Computational Linguistics (2010)
Vulić, I., Moens, M.: Monolingual and cross-lingual information retrieval models based on (bilingual) word embeddings. In: The International ACM SIGIR Conference, pp. 363–372 (2015)
Yang, C., He, B.: A novel semantics-based approach to medical literature search. In: 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1616–1623. IEEE (2016)
Acknowledgments
This work is supported by the National Natural Science Foundation of China (61472391). We would like to thank the authors of [3] for kindly sharing their TREC runs with us.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Yang, C., He, B., Xu, J. (2017). Integrating Feedback-Based Semantic Evidence to Enhance Retrieval Effectiveness for Clinical Decision Support. In: Chen, L., Jensen, C., Shahabi, C., Yang, X., Lian, X. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10367. Springer, Cham. https://doi.org/10.1007/978-3-319-63564-4_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-63564-4_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-63563-7
Online ISBN: 978-3-319-63564-4
eBook Packages: Computer ScienceComputer Science (R0)