Abstract
Changing from a legacy Health Information System (HIS) to a modern HIS creates a problem of migration. Particularly, it requires us to handle unstructured data. In this paper, we proposed a new approach which is used to detect keywords from textual documents, and it involves two stages. Firstly, we study extracting features from the words by exploiting the relation between their characters. The following stage is presenting a combination of inner approach and context-based approach in order to make this extraction. The method is tested with MIMIC-II dataset and, in our problem, it shows a better result compared to old methods. We believe that it can be applied into natural language processing problems in other fields.
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Braunstein, M.L.: Patientphysician collaboration on FHIR (Fast Healthcare Interoperability Resources). In: 2015 International Conference on Collaboration Technologies and Systems (CTS), pp. 501–503. IEEE (2015)
Franz, B., Schuler, A., Kraus, O.: Applying FHIR in an integrated health monitoring system. EJBI 11(2) (2015) en61–56
Habib, J.L.: EHRs, meaningful use, and a model EMR. Drug Benefit Trends 22(4), 99–101 (2010)
Kierkegaard, P.: Electronic health record: wiring Europes healthcare. Comput. Law Secur. Rev. 27(5), 503–515 (2011)
Gunter, T.D., Terry, N.P.: The emergence of national electronic health record architectures in the United States and Australia: models, costs, and questions. J. Med. Internet Res. 7(1) (2005)
Chakrabarti, S., Ester, M., Fayyad, U., Gehrke, J., Han, J., Morishita, S., Piatetsky-Shapiro, G., Wang, W.: Data mining curriculum: a proposal (version 1.0). Intensive Work. Gr. ACM SIGKDD Curric. Comm. 140 (2006)
Han, J., Pei, J., Kamber, M.: Data mining: concepts and techniques. Elsevier (2011)
Hastie, T., Tibshirani, R., Friedman, J.: The elements of statistical learning: data mining, inference, and prediction. Biometrics (2002)
Pujari, A.K.: Data mining techniques. Universities press (2001)
Paulus, D.W., Hornegger, J.: Applied pattern recognition: a practical introduction to image and speech processing in C++. Morgan Kaufmann Publishers (1998)
Burges, C.J.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Disco 2(2), 121–167 (1998)
Hopfield, J.J., et al.: Pattern recognition computation using action potential timing for stimulus representation. Nature 376(6535), 33–36 (1995)
Marsh, E., Perzanowski, D.: Muc-7 evaluation of IE technology: overview of results. In: Seventh Message Understanding Conference (MUC-7): Proceedings of a Conference Held in Fairfax, Virginia, 29 April–1 May 1998. (1998)
Lagerweij, R., Bron, M., Monz, C.: A Joint Classification Approach to Slot-Filling (2012)
Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci. 41(6), 391 (1990)
Mikolov, T., Yih, W.t., Zweig, G.: Linguistic regularities in continuous space word representations. In: HLT-NAACL. vol. 13, pp. 746–751 (2013)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv:1301.3781 (2013)
Rong, X.: Word2vec parameter learning explained. arXiv:1411.2738 (2014)
Rehurek, R., Sojka, P.: Gensim-python framework for vector space modelling. NLP Centre, Faculty of Informatics, Masaryk University, Brno, Czech Republic (2011)
Rehurek, R., Sojka, P.: Gensim–statistical semantics in python (2011)
Goldberger, A.L., Amaral, L.A.N., G.L.H.J.I.P.M.R.M.J.M.G.P.C.K.S.H.: Components of a new research resource for complex physiologic signals. Circulation 101(23):e215–e220 [Circulation Electronic Pages; http://circ.ahajournals.org/content/101/23/e215.full]; 13 June 2000
Powers, D.M.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation (2011)
Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. arXiv:1607.04606 (2016)
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Le, VM., Truong, QN., Huynh, TT. (2018). Combination of Inner Approach and Context-Based Approach for Extracting Feature of Medical Record Data. In: Sieminski, A., Kozierkiewicz, A., Nunez, M., Ha, Q. (eds) Modern Approaches for Intelligent Information and Database Systems. Studies in Computational Intelligence, vol 769. Springer, Cham. https://doi.org/10.1007/978-3-319-76081-0_10
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DOI: https://doi.org/10.1007/978-3-319-76081-0_10
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