The Role of Natural Language Processing (NLP) in Business Intelligence (BI) for Clinical Decision Support
DOI:
https://doi.org/10.63397/ISCSITR-IJBI_2019_01_02_001Keywords:
Natural Language Processing, Clinical Decision Support System, Business Intelligence, Electronic Health Records, Named Entity Recognition, Information Extraction, Healthcare AnalyticsAbstract
Natural Language Processing (NLP) is an essential area of artificial intelligence that allows machines to read, analyze, and create human language. In healthcare, in entities such as Clinical Decision Support Systems (CDSS), NLP transforms by extracting actionable information from unstructured clinical data. This paper investigates the integration of NLP within BI frameworks to advance clinical decision-making processes. The study introduces an all-inclusive look at how NLP leverages the power of BI instruments to transform EHRs, patient notes, medical literature and real-time data streams into intelligent, timely and evidence-based clinical recommendations. This work includes the NLP and BI up to December 2019, describing the key methodologies, tools, and frameworks used in healthcare analytics. This paper presents a comprehensive literature survey, explores methodological approaches, analyses experimental results and offers further work perspectives. In supporting clinical decision-making, attention is given to NLP techniques, namely, Named Entity Recognition (NER), sentiment analysis, topic modelling, and information extraction. Incorporation of NLP within BI enhances the quality of healthcare and operation efficiency; it supports self-enabled medicine and care based on the patient.
References
Meystre, S. M., Savova, G. K., Kipper-Schuler, K. C., & Hurdle, J. F. (2008). Extracting information from textual documents in the electronic health record: a review of recent research. Yearbook of medical informatics, 17(01), 128-144.
Sun, W., Cai, Z., Li, Y., Liu, F., Fang, S., & Wang, G. (2018). Data processing and text mining technologies on electronic medical records: a review. Journal of Healthcare Engineering, 2018(1), 4302425.
Gu, B., Dahl, V., & Popowich, F. (2007, August). Recognizing biomedical named entities in the absence of human-annotated corpora. In 2007 International Conference on Natural Language Processing and Knowledge Engineering (pp. 74-81). IEEE.
Chen, Q., & Sokolova, M. (2018). Word2vec and doc2vec in unsupervised sentiment analysis of clinical discharge summaries. arXiv preprint arXiv:1805.00352.
Niu, L., Lu, J., & Zhang, G. (2009). Cognition-driven decision support for business intelligence. Models, Techniques, Systems and Applications. Studies in Computational Intelligence, Springer, Berlin, 4-5.
Qin, Y., & Zeng, Y. (2018). Research of clinical named entity recognition based on Bi-LSTM-CRF. Journal of Shanghai Jiaotong University (Science), 23, 392-397.
Quimbaya, A. P., Múnera, A. S., Rivera, R. A. G., Rodríguez, J. C. D., Velandia, O. M. M., Peña, A. A. G., & Labbé, C. (2016). Named entity recognition over electronic health records through a combined dictionary-based approach. Procedia Computer Science, 100, 55-61.
Miotto, R., Li, L., Kidd, B. A., & Dudley, J. T. (2016). Deep patient: an unsupervised representation to predict patients' future from the electronic health records. Scientific reports, 6(1), 26094.
Sheikhalishahi, S., Miotto, R., Dudley, J. T., Lavelli, A., Rinaldi, F., & Osmani, V. (2019). Natural language processing of clinical notes on chronic diseases: systematic review. JMIR medical informatics, 7(2), e12239.
Pivovarov, R., & Elhadad, N. (2015). Automated methods for the summarization of electronic health records. Journal of the American Medical Informatics Association, 22(5), 938-947.
Sutton, C., & McCallum, A. (2012). An introduction to conditional random fields. Foundations and Trends® in Machine Learning, 4(4), 267-373.
Zhang, J., Li, J., Wang, S., Zhang, Y., Cao, Y., Hou, L., & Li, X. L. (2018). Category multi-representation: a unified solution for named entity recognition in clinical texts. In Advances in Knowledge Discovery and Data Mining: 22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3-6, 2018, Proceedings, Part II 22 (pp. 275-287). Springer International Publishing.
Du, M. (2017). Natural language processing system for business intelligence.
Liu, S., Duffy, A. H., Whitfield, R. I., & Boyle, I. M. (2010). Integration of decision support systems to improve decision support performance. Knowledge and Information Systems, 22, 261-286.
Bomma, H. P. Natural Language Processing (NLP) In Business Intelligence. IJLRP-International Journal of Leading Research Publication, 5(9).
Iroju, O. G., & Olaleke, J. O. (2015). A systematic review of natural language processing in healthcare. International Journal of Information Technology and Computer Science, 8(8), 44-50.
Hudaa, S., Setiyadi, D. B. P., Lydia, E. L., Shankar, K., Nguyen, P. T., Hashim, W., & Maseleno, A. (2019). Natural language processing utilization in healthcare. International Journal of Engineering and Advanced Technology, 8(6), 1117-1120.
Popowich, F. (2005). Using text mining and natural language processing for health care claims processing. ACM SIGKDD Explorations Newsletter, 7(1), 59-66.
Falessi, D., Cantone, G., & Canfora, G. (2010, September). A comprehensive characterization of NLP techniques for identifying equivalent requirements. In Proceedings of the 2010 ACM-IEEE international symposium on empirical software engineering and measurement (pp. 1-10).
Gupta, V. (2014). A survey of natural language processing techniques. International Journal of Computer Science & Engineering Technology, 5(1), 14-16.
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Copyright (c) 2019 Vijitha Uppuluri (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
