AI-Based Personalized Healthcare Recommendation Systems

  • Authors

    • Dr. Tendai Chikore Department of Civil Engineering, Harare Institute of Technology, Zimbabwe. Author

    DOI:

    https://doi.org/10.67228/30715628/IJMIET-2018PIX9Q4

    Published 02-04-2018

  • Artificial Intelligence, Personalized Healthcare, Recommendation Systems, Machine Learning, Electronic Health Records, Precision Medicine, Predictive Analytics, Healthcare Informatics

    Issue

    Vol. 1 No. 1 (2018)

    Section

    Articles

    How to Cite

    [1]
    T. Chikore, “AI-Based Personalized Healthcare Recommendation Systems”, ijmiet, vol. 1, no. 1, pp. 01–15, Feb. 2018, doi: 10.67228/30715628/IJMIET-2018PIX9Q4.

  • Abstract

    The recent fast progress of artificial intelligence (AI) has changed the general situation in the sphere of healthcare dramatically as the creation of smart systems that can provide individual medical advice is possible. The conventional health care models are largely based on standardized treatment regimens and hence they seldom take into account individual differences that could be in genetic, physiological and behavioral aspects. This drawback has resulted in the development of AI-based personalized healthcare recommendation systems that are intended to give specified interventions, foretelling revelations, and adaptive treatment plans to individual patients. The current paper is a detailed discussion on the AI-based personalized healthcare recommendation systems, their designs, procedures, and uses before 2018. The paper will look at how machine learning algorithms like supervised learning, unsupervised learning and hybrid models have been used to process patient data in the form of electronic health records (EHRs), wearable sensor data, and genomic data. These systems have also been improved in terms of scale and efficiency with the integration of big data analytics and cloud computing. Other critical topics that are being discussed in the paper include data heterogeneity, privacy issues, model interpretability and clinical validation. It particularly focuses on such methods of recommendation as collaborative, content-based, and customized approaches to recommendations. Mathematical expression of prediction model, and measure of similarity are discussed to give a theoretical basis of system design. In addition, the paper measures the performance of the system through measures like accuracy, precision, recall and patient satisfaction indices. A comparative study helps to point out how well AI-based systems can be effective in terms of bettering health results, decreasing readmission rates, and increasing the effectiveness of the decisions made by clinicians. According to the results, AI-powered personalized healthcare can transform the field of patient care and make it proactive, preventive, and precision medicine. Nonetheless, challenges of ethics, regulations and technical issues must be overcome to achieve success in implementation. The conclusion of this paper presents the future directions of research to enhance the robustness, ease-of-interoperability, and clinical adoption of systems.

  • References

    [1] E. H. Shortliffe and B. G. Buchanan, “A model of inexact reasoning in medicine,” Mathematical Biosciences, vol. 23, no. 3–4, pp. 351–379, 1975.

    [2] R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, Machine Learning: An Artificial Intelligence Approach, Springer, 1983.

    [3] T. M. Mitchell, Machine Learning, McGraw-Hill, 1997.

    [4] I. Kononenko, “Machine learning for medical diagnosis: history, state of the art and perspective,” Artificial Intelligence in Medicine, vol. 23, no. 1, pp. 89–109, 2001.

    [5] D. J. Hand, H. Mannila, and P. Smyth, Principles of Data Mining, MIT Press, 2001.

    [6] J. Breese, D. Heckerman, and C. Kadie, “Empirical analysis of predictive algorithms for collaborative filtering,” in Proc. UAI, 1998, pp. 43–52.

    [7] G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems: A survey of the state-of-the-art,” IEEE Trans. Knowledge and Data Engineering, vol. 17, no. 6, pp. 734–749, 2005.

    [8] Y. Koren, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems,” IEEE Computer, vol. 42, no. 8, pp. 30–37, 2009.

    [9] P. Resnick and H. R. Varian, “Recommender systems,” Communications of the ACM, vol. 40, no. 3, pp. 56–58, 1997.

    [10] L. Ricci, B. Shapira, and P. B. Kantor, Recommender Systems Handbook, Springer, 2011.

    [11] S. R. Rashidi and T. S. Cook, “A clinical decision support system for personalized medicine,” IEEE Reviews in Biomedical Engineering, vol. 11, pp. 125–142, 2018.

    [12] A. Esteva et al., “A guide to deep learning in healthcare,” Nature Medicine, vol. 25, pp. 24–29, 2019.

    [13] H. Wang, N. Wang, and D. Yeung, “Collaborative deep learning for recommender systems,” in Proc. ACM KDD, 2015, pp. 1235–1244.

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