International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 7, No. 8, 8 Jul. 2015
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A Systematic Review of Natural Language Processing in Healthcare
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Index Terms
Electronic Healthcare Systems, Healthcare, Natural Language Processing Techniques, Unstructured Information
Abstract
The healthcare system is a knowledge driven industry which consists of vast and growing volumes of narrative information obtained from discharge summaries/reports, physicians case notes, pathologists as well as radiologists reports. This information is usually stored in unstructured and non-standardized formats in electronic healthcare systems which make it difficult for the systems to understand the information contents of the narrative information. Thus, the access to valuable and meaningful healthcare information for decision making is a challenge. Nevertheless, Natural Language Processing (NLP) techniques have been used to structure narrative information in healthcare. Thus, NLP techniques have the capability to capture unstructured healthcare information, analyze its grammatical structure, determine the meaning of the information and translate the information so that it can be easily understood by the electronic healthcare systems. Consequently, NLP techniques reduce cost as well as improve the quality of healthcare. It is therefore against this background that this paper reviews the NLP techniques used in healthcare, their applications as well as their limitations.
Cite This Paper
Olaronke G. Iroju, Janet O. Olaleke, "A Systematic Review of Natural Language Processing in Healthcare", International Journal of Information Technology and Computer Science(IJITCS), vol.7, no.8, pp.44-50, 2015. DOI:10.5815/ijitcs.2015.08.07
Reference
[1]Carol Friedman and Stephen B. Johnson, Natural Language and Text Processing in Biomedicine, United States of America: Springer, 2006, pp.312-343
[2]C. Bock, L. Carnahan, S. Fenves, M. Gruninger, V. Kashyap, B. Lide, J. Nell, R. Raman and R. Sriram, Healthcare Strategic Focus Area: Clinical Informatics. United States of America: National Institute of Standards and Technology, 2005.
[3]C. K. Bretonnel and Lawrence Hunter, “Natural language processing and systems biology,” Artificial Intelligence and Tools for Systems Biology, vol.5, pp. 147-173, 2004.
[4]E. D. Liddy, Natural Language Processing. New York: Encyclopedia of Library and Information Science, Marcel Decker Inc, 2003.
[5]A. Copestake, Natural Language Processing: Part 1 of Lecture Notes. Cambridge: Ann Copestake Lecture Note Series, 2003.
[6]Barzilay Regina and Collins Michael, Natural Language Processing: Background and Overview. Cambridge: Barzilay and Collins Lecture Note Series, 2005.
[7]Rune Saetre, “GeneTUC: Automatic information extraction from biomedical texts”, Proceedings of Computer Science Graduate Students Conference, Norwegian University of Science and Technology (NTNU),Trondheim, Norway, April 29 2004.
[8]Robert Dale, An Introduction to Natural Language Generation. Australia: Microsoft Institute of Advanced Software Technology,2006.
[9]R.C. Chakraborty, Natural Language Processing Artificial Intelligence. United Sates of America: Chakraborty Lecture Note Series, 2010.
[10]D. Jurafsky and J.H. Martin, Speech and Language Processing. Englewood Cliffs: Prentice-Hall, 2008.
[11]Bates Madeleine, “Models of natural language understanding”. Proceedings of National Academy Science, USA, 1995, pp. 9977-9982.
[12]Moschitti Alessandro, Natural Language Processing and Automated Text Categorization. A Study on the Reciprocal Beneficial Interactions. Rome: University of Rome, 2003.
[13]F. Jensen, An Introduction to Bayesian Networks. Springer Verlag, 1996.
[14]Sujan Perera, Amit Sheth and Krishnaprasad Thirunarayan. “Challenges in understanding clinical notes: why nlp engines fall short and where background knowledge can help”. Proceedings of ACM Conference of Information and Management, Burlingame, November 1, 2013.
[15]Vishal Gupta and Gurpreet Lehal, “A survey of text mining techniques and applications,” Journal of Emerging Technologies in Web Intelligence,vol. 1, pp. 60-76, 2009.
[16]K. Iman and S.A. Mohammad, “A metric-based approach for web-based question answering,” International Journal of Information Technology and Computer Science, vol. 9, pp, 39-45, 2014.
[17]D. Radev, K. Hovy, and K. McKeown, “Introduction to the special issue on summarization. Computational Linguistics,vol. 28, pp. 399-408, 2002.
[18]C.D. Manning and H. Schütze, Foundations of Statistical Natural Language Processing. Cambridge: MIT Press.
[19]E.A. Mendonca, J. Haas, L . Shagina, E. Larson and C Friedman, “Extracting information on pneumonia in infants using natural language processing of radiology reports”, Journal of Biomedical Informatics, vol. 38, pp. 314:321,2005.
[20]W. Chapman, J. Dowling, O. Ivanov, P. Gesteland, R. Olszewski, U. Espino and N. Wagner, Evaluating Natural Language Processing Applications Applied to Outbreak and Disease Surveillance, USA: RODS Laboratory, Center for Biomedical Informatics,University of Pittsburgh, 2004.
[21]Meystre Stéphane and Haug Peter. “Comparing natural language processing tools to extract medical problems from narrative text. Proceedings of AMIA Symposium, pp. 525-529, 2005.
[22]B. Kusenda, Introduction to NLP with cTAKES. Boston: Mayo Clinic, 2012.
[23]Rune Saetre. GeneTUC: Natural Language Understanding in Medical Text. Norway: Department of Computer and Information Science, Norwegian University of Science and Technology (NTNU),Trondheim,2006.
[24]A. Aronson, “Effective mapping of biomedical text to the UMLS metathesaurus: The MetaMap Program”, Proceedings of AMIA Symposium, pp. 17-21, 2001.
[25]Nadkarni Prakash, Ohno- Machado Lucila, and Chapman Wendy. Natural language processing: An introduction. Journal of American Medical Information Association, vol. 18, pp.544-551, 2011.
[26]O. Bodenreider, B. Smith, A. Kumar and A. Burgun, “Investigating subsumption in dl-based terminologies: A case study in SNOMED-CT”, First International Workshop on Formal Biomedical Knowledge Representation, pp.12-20, 2004.
[27]P. Waraporn, P. Meesad and G. Clayton. “Proposed ontology based knowledge and integration framework”. International Journal of Computer Science and Network Security, vol. 10 ,pp. 30-36, 2010.
[28]N. Fabozzi, Kaiser’s Donation of its Convergent Medical Terminology Dictionary Puts the Spotlight on the Role of Clinical Terminology Services in Driving Meaningful Use of EHRs . Healthcare and Life Sciences, Frost and Sullivan,2009.
[29]Calin Cenan, Gheorghe Sebestyen, Gavril Saplacan, Dan Radulescu. Ontology-Based Distributed Health Record Management System, Department of Computer Science, Technical University of Cluj, Napoca,2004.
[30]G. Vadivu, and S.H. Waheeta, “Ontology mapping of Indian medicinal plants with standardized medical terms”, Journal of Computer Science, vol. 8, pp. 1576-1584, 2013.
[31]P Hyeoun-Ae. And H. Nick, “Clinical terminologies: A solution for semantic interoperability, Journal of Korean Society of Medical Informatics, vol. 15(1), pp. 1-11, 2009.