Abstract
Many medical tasks such as self-diagnosis, health-care assessment, and clinical trial patient recruitment involve the usage of information access tools. A key underlying step to achieve such tasks is the document-to-document matching which mostly fails to bridge the gap identified between raw level representations of information in documents and high-level human interpretation. In this paper, we study how to optimize the document representation by leveraging neural-based approaches to capture latent representations built upon both validated medical concepts specified in an external resource as well as the used words. We experimentally show the effectiveness of our proposed model used as a support of two different medical search tasks, namely health search and clinical search for cohorts.
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.
Text Retrieval Conference (http://trec.nist.gov/).
- 2.
References
Abdou, S., Savoy, J.: Searching in MEDLINE: query expansion and manual indexing evaluation. Inf. Process. Manag. 44(2), 781–789 (2008)
Bordes, A., Usunier, N., García-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: NIPS (2013)
Choi, E., Bahadori, M.T., Searles, E., Coffey, C., Sun, J.: Multi-layer representation learning for medical concepts. In: KDD, pp. 1495–1504 (2016)
De Vine, L., Zuccon, G., Koopman, B., Sitbon, L., Bruza, P.: Medical semantic similarity with a neural language model. In: CIKM, pp. 1819–1822 (2014)
Dinh, D., Tamine, L.: Combining global and local semantic contexts for improving biomedical information retrieval. In: Clough, P., Foley, C., Gurrin, C., Jones, G.J.F., Kraaij, W., Lee, H., Mudoch, V. (eds.) ECIR 2011. LNCS, vol. 6611, pp. 375–386. Springer, Heidelberg (2011). doi:10.1007/978-3-642-20161-5_38
Edinger, N.T., Cohen, A.M., Bedrick, S., Ambert, K., Hersh, W.: Barriers to retrieving patient information from electronic health record data: failure analysis from the TREC medical records track. In: AMIA Annual Symposium, pp. 180–188 (2012)
Faruqui, M., Dodge, J., Jauhar, S.K., Dyer, C., Hovy, E., Smith, N.A.: Retrofitting word vectors to semantic lexicons. In: NAACL (2015)
Gobeill, J., Ruch, P., Zhou, X.: Query and document expansion with medical subject headings terms at medical Imageclef 2008. In: Peters, C., Deselaers, T., Ferro, N., Gonzalo, J., Jones, G.J.F., Kurimo, M., Mandl, T., Peñas, A., Petras, V. (eds.) CLEF 2008. LNCS, vol. 5706, pp. 736–743. Springer, Heidelberg (2009). doi:10.1007/978-3-642-04447-2_95
Hofmann, T.: Probabilistic latent semantic indexing. In: SIGIR, pp. 50–57 (1999)
Iacobacci, I., Pilehvar, M.T., Navigli, R.: Sensembed: learning sense embeddings for word and relational similarity. In: ACL, pp. 95–105 (2015)
Koopman, B., Zuccon, G., Bruza, P., Sitbon, L., Lawley, M.: Information retrieval as semantic inference: a graph inference model applied to medical search. Inf. Retrieval 19(1–2), 6–37 (2016)
Le, Q.V., Mikolov, T.: Distributed representations of sentences and documents. In: ICML, pp. 1188–1196 (2014)
Le, T.-D., Chevallet, J.-P., Dong, T.B.T.: Thesaurus-based query and document expansion in conceptual indexing with UMLS. In: RIVF 2007, pp. 242–246 (2007)
Lu, Z., Kim, W., Wilbur, W.J.: Evaluation of query expansion using MeSH in PubMed. Inf. Retrieval 12(1), 69–80 (2009)
Mao, J., Lu, K., Mu, X., Li, G.: Mining document, concept, and term associations for effective biomedical retrieval: introducing MeSH-enhanced retrieval models. Inf. Retrieval 18(5), 413–444 (2015)
Marton, C., Choo, C.W.: A review of theroretical models on health information seeking on the web. J. Documentation 68(3), 330–352 (2012)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint (2013). arXiv:1301.3781
Minarro-Gimenez, J., Marin-Alonso, O., Samwald, M.: Exploring the application of deep learning techniques on medical text corpora. Stud. Health Technol. Inf. 205, 584–588 (2014)
Ni, Y., Xu, Q.K., Cao, F., Mass, Y., Sheinwald, D., Zhu, H.J., Cao, S.S.: Semantic documents relatedness using concept graph representation. In: WSDM (2016)
Pal, D., Mitra, M., Datta, K.: Improving query expansion using wordnet. JASIST 65(12), 2469–2478 (2014)
Rocchio, J.J.: Relevance feedback in information retrieval. In: The SMART Retrieval System, pp. 313–323 (1971)
Stokes, N., Cavedon, Y., Zobel, J.: Exploring criteria for succesful query expansion in the genomic domain. Inf. Retrieval 12, 17–50 (2009)
Trieschnigg, D.: Proof of concept: concept-based biomedical information retrieval. Ph.D. thesis. University of Twente (2010)
Voorhees, E., Hersh, W.: Overview of the TREC medical records track. In: TREC (2012)
Wang, C., Akella, R.: Concept-based relevance models for medical and semantic information retrieval. In: CIKM, pp. 173–182 (2015)
Wang, S., Hauskrecht, M.: Effective query expansion with the resistance distance based term similarity metric. In: SIGIR, pp. 715–716 (2010)
Liu, X., Nie, J.-Y., Sordoni, A.: Constraining word embeddings by prior knowledge – application to medical information retrieval. In: Ma, S., Wen, J.-R., Liu, Y., Dou, Z., Zhang, M., Chang, Y., Zhao, X. (eds.) AIRS 2016. LNCS, vol. 9994, pp. 155–167. Springer, Cham (2016). doi:10.1007/978-3-319-48051-0_12
Xu, C., Bai, Y., Bian, J., Gao, B., Wang, G., Liu, X., Liu, T.-Y.: Rc-net: a general framework for incorporating knowledge into word representations. In: CIKM (2014)
Yu, M., Dredze, M.: Improving lexical embeddings with semantic knowledge. In: ACL, pp. 545–550 (2014)
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
Nguyen, GH., Tamine, L., Soulier, L., Souf, N. (2017). Learning Concept-Driven Document Embeddings for Medical Information Search. In: ten Teije, A., Popow, C., Holmes, J., Sacchi, L. (eds) Artificial Intelligence in Medicine. AIME 2017. Lecture Notes in Computer Science(), vol 10259. Springer, Cham. https://doi.org/10.1007/978-3-319-59758-4_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-59758-4_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59757-7
Online ISBN: 978-3-319-59758-4
eBook Packages: Computer ScienceComputer Science (R0)