The Role of Predictive Analytics in Disease Prevention : A Technical Overview

Harpreet Singh

doi:10.32628/CSEIT24106174

Authors

Harpreet Singh Gilead Sciences, USA Author

DOI:

https://doi.org/10.32628/CSEIT24106174

Keywords:

Predictive Analytics, Healthcare AI, Disease Prevention, Risk Stratification, Data Integration

Abstract

This article explores the transformative potential of predictive analytics in healthcare, focusing on its applications in disease prevention and public health management. It examines the power of data integration from diverse sources, the use of predictive modeling for risk stratification, and the broader implications for public health surveillance and chronic disease management. The article also discusses the significant growth of AI in the healthcare market and highlights successful implementations of predictive analytics across various medical domains. Additionally, it addresses the key challenges in implementing these technologies, including data privacy concerns, integration issues, model accuracy, and ethical considerations. Through numerous case studies and statistical evidence, the article demonstrates how predictive analytics is revolutionizing healthcare by enabling more accurate, personalized, and proactive approaches to disease prevention and management.

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References

A. Davenport and R. Kalakota, "The potential for artificial intelligence in healthcare," Future Healthcare Journal, vol. 6, no. 2, pp. 94–98, 2019. [Online]. Available: https://doi.org/10.7861/futurehosp.6-2-94 DOI: https://doi.org/10.7861/futurehosp.6-2-94

M. A. Krakower et al., "Development and Validation of an Automated HIV Prediction Algorithm to Identify Candidates for Pre-exposure Prophylaxis: A Modelling Study," The Lancet HIV, vol. 6, no. 10, pp. e696-e704, 2019. [Online]. Available: https://doi.org/10.1016/S2352-3018(19)30139-0 DOI: https://doi.org/10.1016/S2352-3018(19)30139-0

J. C. Ho, J. Ghosh, and J. Sun, "Marble: High-throughput phenotyping from electronic health records via sparse nonnegative tensor factorization," in Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014, pp. 115-124. [Online]. Available: https://doi.org/10.1145/2623330.2623658 DOI: https://doi.org/10.1145/2623330.2623658

B. Shickel, P. J. Tighe, A. Bihorac, and P. Rashidi, "Deep EHR: A survey of recent advances in deep learning techniques for electronic health record (EHR) analysis," IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 5, pp. 1589-1604, 2018. [Online]. Available: https://doi.org/10.1109/JBHI.2017.2767063 DOI: https://doi.org/10.1109/JBHI.2017.2767063

B. K. Beaulieu-Jones et al., "Machine learning for patient risk stratification: standing on, or looking over, the shoulders of clinicians?," npj Digital Medicine, vol. 4, no. 1, pp. 1-6, 2021. [Online]. Available: https://doi.org/10.1038/s41746-021-00426-3 DOI: https://doi.org/10.1038/s41746-021-00426-3

A. Rajkomar et al., "Scalable and accurate deep learning with electronic health records," npj Digital Medicine, vol. 1, no. 1, pp. 1-10, 2018. [Online]. Available: https://doi.org/10.1038/s41746-018-0029-1 DOI: https://doi.org/10.1038/s41746-018-0029-1

C. Schwalbe and B. Wahl, "Artificial intelligence and the future of global health," The Lancet, vol. 395, no. 10236, pp. 1579-1586, 2020. [Online]. Available: https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30226-9/fulltext DOI: https://doi.org/10.1016/S0140-6736(20)30226-9

M. A. Mahmud et al., "Applications of artificial intelligence in battling against covid-19: A literature review," Chaos, Solitons & Fractals, vol. 142, p. 110338, 2021. [Online]. Available: https://doi.org/10.1016/j.chaos.2020.110338 DOI: https://doi.org/10.1016/j.chaos.2020.110338

C. Krittanawong et al., "Artificial Intelligence in Precision Cardiovascular Medicine," Journal of the American College of Cardiology, vol. 69, no. 21, pp. 2657-2664, 2017. [Online]. Available: https://doi.org/10.1016/j.jacc.2017.03.571 DOI: https://doi.org/10.1016/j.jacc.2017.03.571

T. Panch, H. Mattie, and L. A. Celi, "The "inconvenient truth" about AI in healthcare," npj Digital Medicine, vol. 2, no. 1, pp. 1-3, 2019. [Online]. Available: https://doi.org/10.1038/s41746-019-0155-4 DOI: https://doi.org/10.1038/s41746-019-0155-4

J. He et al., "The practical implementation of artificial intelligence technologies in medicine," Nature Medicine, vol. 25, no. 1, pp. 30-36, 2019. [Online]. Available: https://doi.org/10.1038/s41591-018-0307-0 DOI: https://doi.org/10.1038/s41591-018-0307-0

M. Ghassemi, T. Naumann, P. Schulam, A. L. Beam, I. Y. Chen, and R. Ranganath, "A review of challenges and opportunities in machine learning for health," AMIA Summits on Translational Science Proceedings, vol. 2020, p. 191, 2020. [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233077/