Using Edge AI and Language Understanding for Predictive Modeling of Acute Medical Intoxications

Authors

  • Strahil Sokolov University of Telecommunications and Post
  • Stanislava Georgieva Medical university of Varna

DOI:

https://doi.org/10.48149/jciees.2021.1.2.3

Keywords:

NLP, Edge AI, medical documents, medicinal intoxication

Abstract

This paper presents a new approach to processing and categorization of text from patient documents in Bulgarian language using Natural Language Processing and Edge AI. The proposed algorithm contains several phases - personal data anonymization, pre-processing and conversion of text to vectors, model training and recognition. The experimental results in terms of achieved accuracy are comparable with modern approaches.

Metrics

Metrics Loading ...

References

Georgieva S., Marinov P. (2019). Nutritional Toxicology – An Overview, Proceedings of University of Ruse, Vol. 58, Book 10.2, pp. 61-66 (in Bulgarian).

Georgieva S., Agova N. (2020). Risk Of Liver Injury During Use Of Dietary Supplements, Medicine, Pharmacy, Public Health, pp 48-51, 2020.

Abacha, Asma Ben, and Pierre Zweigenbaum (2015). MEANS: A medical question-answering system combining NLP techniques and semantic Web technologies, Information processing & management 51, no. 5, pp. 570-594.

Lee, Yen-Lin, Pei-Kuei Tsung, and Max Wu (2018). Techology trend of edge AI, in 2018 International Symposium on VLSI Design, Automation and Test (VLSI-DAT), pp. 1-2.

Shi, Y., Yang, K., Jiang, T., Zhang, J. and Letaief, K.B. (2020). Communication-efficient edge AI: Algorithms and systems, in IEEE Communications Surveys & Tutorials, 22(4), pp. 2167-2191.

Dhole, Gunjan, and Nilesh Uke. (2014). NLP based retrieval of medical information for diagnosis of human diseases, in Int J Renew Energy Technol 3, no. 10, 243e8.

Razno, M. (2019). Machine learning text classification model with NLP approach, Computational Linguistics and Intelligent Systems 2, pp. 71-73.

Afshar, Majid, Dmitriy Dligach, Brihat Sharma, Xiaoyuan Cai, Jason Boyda, Steven Birch, Daniel Valdez et al. (2019). "Development and application of a high throughput natural language processing architecture to convert all clinical documents in a clinical data warehouse into standardized medical vocabularies." Journal of the American Medical Informatics Association 26, no. 11: 1364-1369.

Nelli, F. (2018) "Machine Learning with scikit-learn.", In Python Data Analytics, Apress, Berkeley, CA, pp. 313-347.

Bisong, Ekaba. (2019) "Introduction to Scikit-learn." in Building Machine Learning and Deep Learning Models on Google Cloud Platform, Apress, Berkeley, CA, pp. 215-229.

Reiss, F., Cutler, B. and Eichenberger, Z., (2021) Natural Language Processing with Pandas DataFrames, Proc. Of The 20th Python In Science Conf. (Scipy 2021), pp. 49-58.

Sokolov, S. (2018). Neural Network Based Multimodal Emotion Estimation." ICAS 2018 - The 14th International Conference on Autonomic and Autonomous Systems 12, pp. 4-7.

Cass, S. (2020). Nvidia makes it easy to embed AI: The Jetson nano packs a lot of machine-learning power into DIY projects-[Hands on]." IEEE Spectrum 57, no. 7, pp. 14-16.

Chary, Michael, Ed W. Boyer, and Michele M. Burns. (2021) Diagnosis of Acute Poisoning using explainable artificial intelligence." Computers in Biology and Medicine 134: 104469.

Özdikililer, E. and Göksel, Ç., (2018). Design a Model for Integrated Information Systems: DataOCEAN©, Journal of Geomatics, 3 (3), pp. 225-232. doi: 10.29128/geomatik.406294.

Sokolov, S. (2020). Deep Neural Network Based Processing of Medical Records in Cloud Environment”, Proceedings of 5th International Conference TIEM, University of Telecommunications and Post, Sofia, pp. 104-108.

Downloads

Published

2021-12-22

How to Cite

Sokolov, S., & Georgieva, S. (2021). Using Edge AI and Language Understanding for Predictive Modeling of Acute Medical Intoxications. The Journal of CIEES, 1(2), 18–22. https://doi.org/10.48149/jciees.2021.1.2.3