A Semantic Multi-Field Clinical Search for Patient Medical Records

1. Langville, A. N., C. D. Meyer. Google’s PageRank and Beyond: The Science of Search Engine Rankings. Princeton University Press, 2011.Search in Google Scholar

2. Bodenreider, O. The Unified Medical Language System (UMLS): Integrating Biomedical Terminology. – Nucleic Acids Research, Vol. 32, 2004, Suppl. 1, pp. D267-D270.10.1093/nar/gkh06130879514681409Search in Google Scholar

3. Manning, C. D., P. Raghavan, H. Schutze. Introduction to Information Retrieval. Vol. 1. Cambridge, Cambridge University Press, 2008.Search in Google Scholar

4. Cummins, R. Clinical Decision Support with the SPUD Language Model. TREC, 2015.Search in Google Scholar

5. Klein, D., C. D. Manning. Accurate Unlexicalized Parsing. – In: Proc. of 41st Annual Meeting on Association for Computational Linguistics, Vol. 1, Association for Computational Linguistics, 2003, pp. 423-430.10.3115/1075096.1075150Search in Google Scholar

6. Metzler, D., W. B. Croft. A Markov Random Field Model for Term Dependencies. – In: Proc. of 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, 2005, pp. 472-479.10.1145/1076034.1076115Search in Google Scholar

7. Zhu, D., S. T.-I. Wu, J. J. Masanz, B. Carterette, H. Liu. Using Discharge Summaries to Improve Information Retrieval in Clinical Domain. – In: CLEF (Working Notes), 2013.Search in Google Scholar

8. Zhu, D., B. Carterette. Improving Health Records Search Using Multiple Query Expansion Collections. – In: 2012 IEEE International Conference on Bioinformatics and Biomedicine (BIBM’12), IEEE, 2012, pp. 1-7.10.1109/BIBM.2012.6392667Search in Google Scholar

9. Diaz, F., D. Metzler. Improving the Estimation of Relevance Models Using Large External Corpora. – In: Proc. of 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, 2006, pp. 154-161.10.1145/1148170.1148200Search in Google Scholar

10. Kilicoglu, H., D. Shin, M. Fiszman, G. Rosemblat, T. C. Rindflesch. Semmeddb: A Pubmed-Scale Repository of Biomedical Semantic Predications. Bioinformatics, Vol. 28, 2012, No 23, pp. 3158-3160.10.1093/bioinformatics/bts591350948723044550Search in Google Scholar

11. Oh, H.-S., Y. Jung, K.-Y. Kim. A Multiple-Stage Approach to Re-Ranking Medical Documents. – In: International Conference of the Cross-Language Evaluation Forum for European Languages, Springer, 2015, pp. 166-177.10.1007/978-3-319-24027-5_14Search in Google Scholar

12. Fung, K. W., M. Ma, S. Srinivasan. The Umls-Core Project – A Study of the Problem List Vocabularies Used in Large Health Care Institutions, 2010.10.1136/jamia.2010.007047300076220962130Search in Google Scholar

13. Robertson, S. E., S. Walker. Some Simple Effective Approximations to the Poisson Model for Probabilistic Weighted Retrieval. – Readings in Information Retrieval, 1997, p. 345.Search in Google Scholar

14. Jonnalagadda, S. R., G. D. Fiol, R. Medlin, C. Weir, M. Fiszman, J. Mostafa, H. Liu. Automatically Extracting Sentences from Medline Citations to Support Clinicians’ Information Needs. – Journal of the American Medical Informatics Association, Vol. 20, 2013, No 5, pp. 995-1000.10.1136/amiajnl-2012-001347375625923100128Open DOISearch in Google Scholar

15. Lavrenko, V., W. B. Croft. Relevance Based Language Models. – In: Proc. of 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, 2001, pp. 120-127.10.1145/383952.383972Search in Google Scholar

16. Xia, Y., H. Zhao, K. Liu, H. Zhu. Normalization of Chinese Informal Medical Terms Based on Multi-Field Indexing. – In: Natural Language Processing and Chinese Computing, Springer, 2014, pp. 311-320.10.1007/978-3-662-45924-9_28Search in Google Scholar