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Adding AI to the Decision Support System used in Patient Health Assessment

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Published:20 May 2019Publication History

ABSTRACT

This paper describes one solution for embedding an Artificial Intelligence (AI) framework into the Decision Support System (DSS) used for a patient health assessment and life improvement. The DSS processes sensor-acquired health parameters and various health-related data in order to give an assessment of the patient health condition as well as to try to predict the possibility of the patient having another major health decline, such as stroke. The initial DSS version processes data using the statistical methods and proprietary algorithms which did not employ AI techniques. Rapid emergence of the AI frameworks, and evidence of clearly noticeable better results for systems based on AI, motivated us to integrate AI into the DSS. In this paper we present integration of TensorFlow framework in the DSS.

References

  1. E. Turban. 1993. Decision Support and Expert Systems: Management Support Systems (3rd ed.). Prentice Hall PTR, Upper Saddle River, NJ, USA. ISBN:0024216917 Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. M. Vidaković, S. Ćosić, O. Ćosić, I. Kaštelan and G. Velikić. 2018. One solution for execution of JavaScript in Java EE application servers. In Proceedings of the Zooming Inovation in Consumer Technologies Conference (ZINC 2018). pp. 177--180.Google ScholarGoogle ScholarCross RefCross Ref
  3. "Decision Support and Self-Management System for Stroke Survivors" (STARR), http://www.starrproject.org/ (accessed on 25 Feb 2019)Google ScholarGoogle Scholar
  4. R. Baptista, E. Ghorbel, A. Shabayek, D. Aouada and B. Ottersten. 2018. Key-Skeleton Based Feedback Tool for Assisting Physical Activity. In Proceedings of the Zooming Inovation in Consumer Technologies Conference (ZINC 2018). pp. 175--176.Google ScholarGoogle ScholarCross RefCross Ref
  5. C. Magnusson, M. Anastassova, S. Paneels, K. Rassmus-Gröhn, B. Rydeman, G. Randall, L. Ortiz Fernandez, S. Bouilland, J. Pager and P. Olof Hedvall. 2018. Stroke and universal design. In Proceedings of Universal Design and Higher Education in Transformation Congress 2018. pp. 854--861Google ScholarGoogle Scholar
  6. M. Abadi et al. 2016. TensorFlow: A System for Large-Scale Machine Learning. In Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 2016). pp. 265--283 Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. "Top 9 Frameworks in the World of Artificial Intelligence", https://geekflare.com/ai-frameworks/ (accessed on 25 Feb 2019)Google ScholarGoogle Scholar
  8. "Top 10 Trending Artificial Intelligence Frameworks and Libraries", https://hackernoon.com/top-10-trending-artificial-intelligence-frameworks-and-libraries-69ba59057a78 (accessed on 25 Feb 2019)Google ScholarGoogle Scholar
  9. P. Su, X. Ding, Y. Zhang, J. Liu, F. Miao and N. Zhao. 2018. Long-term blood pressure prediction with deep recurrent neural networks. In Proceedings of the International Conference on Biomedical & Health Informatics (BHI 2018).Google ScholarGoogle Scholar
  10. M. Ture, I. Kurt, A.T. Kurum and K. Ozdamar. 2005. Comparing classification techniques for predicting essential hypertension, In Expert Systems with Applications, Vol. 29, Issue 3. pp. 583--588. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. R. Voss, P. Cullen, H. Schulte and G. Assmann. Prediction of risk of coronary events in middle-aged men in the Prospective Cardiovascular Münster Study (PROCAM) using neural networks. In International Journal of Epidemiology, Vol. 31, Issue 6. pp. 1253--1262.Google ScholarGoogle ScholarCross RefCross Ref
  12. Personal Health Tracker, http://zuum.wustl.edu/ (accessed on 25 Feb 2019)Google ScholarGoogle Scholar
  13. S. Ruder. 2017. An overview of gradient descent optimization algorithms. https://arxiv.org/abs/1609.04747 (accessed on 25 Feb 2019)Google ScholarGoogle Scholar

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    • Published in

      cover image ACM Other conferences
      PervasiveHealth'19: Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare
      May 2019
      475 pages
      ISBN:9781450361262
      DOI:10.1145/3329189

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      New York, NY, United States

      Publication History

      • Published: 20 May 2019

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      Overall Acceptance Rate55of116submissions,47%

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