DETECTION OF MEDICAL INFORMATION USING LSTM
Authors:
Gi Sung Lee, Division of Computer & Game, Howon University, Korea
Jong Chan Lee, Dept. of Computer Information Engineering, Kunsan National University, Korea
Abstract:
The growing number of medical consumers aiming for a better quality of life is boosting the Online to Offline (O2O) medical marketing industry, which saves time and money of clients by choosing reliable medical facilities and receiving high-quality medical services based on medical information from blogs distributed on the Web. Unstructured data generated from the Internet, mobile, SNS, etc., cannot guarantee the credibility of medical information because it directly or indirectly reflects the interest, preference, and expectation of the author in addition to professional medical knowledge. In this study, we propose a blog detection system that provides users with a higher quality medical information service by detecting medical blogs using big data and Long Short-Term Memory (LSTM) processing. We collect and analyze various medical blogs on the Internet based on the big data and machine learning technology, and develop a personalized health recommendation system for each disease. It is expected that the users will be able to maintain their health condition by continuously checking their health problems and taking the most appropriate measures.
Keywords:
Big data, Long Short-Term Memory, TF-IDF, Doc2Vec, Medical blog
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Citations:
APA:
Lee, G. S., & Lee, J. C. (2019). Detection of Medical Information using LSTM. International Journal of Advanced Science and Technology (IJAST), ISSN: 2005-4238(Print); 2207-6360 (Online), NADIA, 130, 49-58. doi: 10.33832/ijast.2019.130.05.
MLA:
Lee, Gi Sung, et al. “Detection of Medical Information using LSTM.” International Journal of Advanced Science and Technology, ISSN: 2005-4238(Print); 2207-6360 (Online), NADIA, vol. 130, 2019, pp. 49-58. IJAST, http://article.nadiapub.com/IJAST/Vol130/5.html.
IEEE:
[1] G. S. Lee and J. C. Lee, “Detection of Medical Information using LSTM.” International Journal of Advanced Science and Technology (IJAST), ISSN: 2005-4238(Print); 2207-6360 (Online), NADIA, vol. 130, pp. 49-58, Sep. 2019.
