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  • E-ISSN:

    2454-9584

    P-ISSN

    2454-8111

    Impact Factor 2020

    5.051

    Impact Factor 2021

    5.610

  • E-ISSN:

    2454-9584

    P-ISSN

    2454-8111

    Impact Factor 2020

    5.051

    Impact Factor 2021

    5.610

  • E-ISSN:

    2454-9584

    P-ISSN

    2454-8111

    Impact Factor 2020

    5.051

    Impact Factor 2021

    5.610

INTERNATIONAL JOURNAL OF INVENTIONS IN ENGINEERING & SCIENCE TECHNOLOGY

International Peer Reviewed (Refereed), Open Access Research Journal
(By Aryavart International University, India)

Paper Details

Machine Learning in Healthcare: Application and Challenges

Aaruksha Dahiya

Aristotle Public Sr.Sec School, Bus Stand, Qutabgarh, Delhi, 110039

9 - 14 Vol. 9, Jan-Dec, 2023
Receiving Date: 2022-11-08;    Acceptance Date: 2023-01-05;    Publication Date: 2023-01-19
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Abstract

Coordinating AI (ML) techniques in medical services has arisen as an unexpected strength, upsetting different parts of patient consideration, ailing the executives, and medical services activities. This research paper investigates the complex applications and the difficulties of using ML in medical services. AI finds broad application in medical services, enveloping early disease identification, customized therapy plans, drug detection, clinical image examination, and patient risk separation. It is essential in clinical decision help, upgrading analytic precision and treatment adequacy. Besides, ML-based telemedicine and remote observing arrangements have extended medical services availability, especially in remote or underserved regions. Even with its exceptional potential, testing ML in medical services. Information protection and security concerns are central as delicate patient data is handled. Information quality, interoperability issues, and moral contemplations connected with algorithm inclination and direct request watchful consideration. Management obstacles and protection from change among medical services experts add intricacy to the mixed interaction. Moral contemplations arise unmistakably as medical service suppliers progressively depend on ML-driven experiences. This paper talks about the ethical aspects encompassing patient information protection, informed consent, and the requirement for transparent and neutral algorithm. 2023 rolex replica top replica watches UK are in stock. You can possess best fake watches with less money.
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Keywords: AI Techniques; medical services; Machine Learning

    References

  1. Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118.
  2. Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., ... & Sanchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical image analysis, 42, 60-88.
  3. Liu, Y., Wang, W., Zhao, M., Tu, R., & Li, X. (2020). PICO-grams: A systematic evaluation framework for developing and evaluating clinical questions. Journal of Biomedical Informatics, 103, 103389.
  4. Zhao, J., Jin, X., Xiao, Y., Zheng, Y., & Lei, T. (2020). Personalized immunosuppressive drug dosing for kidney transplant patients using the integration of clinical information and omics data. Briefings in Bioinformatics, 21(6), 2169-2180.
  5. Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., ... & Zhang, M. (2018). Scalable and accurate deep learning with electronic health records. NPJ Digital Medicine, 1(1), 1-10.
  6. Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future-big data, machine learning, and clinical medicine. New England Journal of Medicine, 375(13), 1216-1219.
  7. Beam, A. L., & Kohane, I. S. (2018). Big data and machine learning in health care. Jama, 319(13), 1317-1318.
  8. Miotto, R., Li, L., Kidd, B. A., & Dudley, J. T. (2016). Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Scientific reports, 6, 1-10.
  9. Ghassemi, M., Naumann, T., Schulam, P., Beam, A. L., Chen, I. Y., Ranganath, R., & Ghassemi, M. M. (2018). A review of challenges and opportunities in machine learning for health. AMIA Summits on Translational Science Proceedings, 2018, 191.
  10. Johnson, A. E., Pollard, T. J., Shen, L., Lehman, L. H., Feng, M., Ghassemi, M., ... & Celi, L. A. (2016). MIMIC-III, a freely accessible critical care database. Scientific data, 3, 1-9.
  11. Char, D. S., Shah, N. H., & Magnus, D. (2019). Implementing machine learning in health care—addressing ethical challenges. New England Journal of Medicine, 378(11), 981-983.
  12. Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2017). Deep EHR: A survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE journal of biomedical and health informatics, 22(5), 1589-1604.
  13. Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., ... & Lungren, M. P. (2017). Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv preprint arXiv:1711.05225.
  14. Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453.
  15. Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., & Elhadad, N. (2015). Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, 1721-1730.
  16. Goldenberg, A. J., Zhang, X., Keane, T., & Zou, J. (2017). Integrating electronic health record information improves gene expression prediction of therapeutic response. Pacific Symposium on Biocomputing, 76-87.
  17. Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018). Deep learning for healthcare: review, opportunities and challenges. Briefings in Bioinformatics, 19(6), 1236-1246.
  18. Ravi, D., Wong, C., Lo, B., & Yang, G. Z. (2017). Deep learning for human motion analysis: Hand, head, and body. IEEE transactions on pattern analysis and machine intelligence, 40(8), 1862-1877.
  19. Obermeyer, Z., Powers, B., & Vogeli, C. (2020). Dissecting risk factors for COVID-19 transmission. Nature Medicine, 26(6), 811-812.
  20. Chen, K., Song, L., Bai, L., Zhang, L., & Chen, K. (2019). Federated learning in mobile edge networks: A comprehensive survey. IEEE Communications Surveys & Tutorials, 22(3), 2031-2063.
  21. Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., ... & Webster, D. R. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, 316(22), 2402-2410.
  22. Janowczyk, A., & Madabhushi, A. (2016). Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases. Journal of Pathology Informatics, 7, 29.
  23. Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2017). Deep EHR: A survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE journal of biomedical and health informatics, 22(5), 1589-1604.
  24. Schneider, P., Walters, W. P., & Plowright, A. T. (2020). Rethinking drug design in the artificial intelligence era. Nature Reviews Drug Discovery, 19(5), 353- 364.
  25. Wasfy, J. H., Rao, S. K., Kalagara, R., Chittle, M. D., & Richardson, C. A. (2020). Telemedicine expansion during the COVID-19 pandemic and the potential for technology-driven disparities. Journal of Medical Internet Research, 22(12), e20044.
  26. Denny, J. C., & Malin, B. (2016). Protecting patient privacy when sharing patient-level data for research. Science translational medicine, 8(322), 322ra7.
  27. Norgeot, B., Quer, G., Beaulieu-Jones, B. K., Torkamani, A., & Dias, R. (2019). Minimum information about clinical artificial intelligence modeling: the MICLAIM checklist. Nature Medicine, 25(9), 1313-1317.
  28. Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453.
  29. Wachter, S., & Mittelstadt, B. (2019). A right to reasonable inferences: re-thinking data protection law in the age of big data and AI. Columbia Business Law Review, 2019(2), 494-571.
  30. Huckvale, K., Prieto, J. T., Tilney, M., Benghozi, P. J., & Car, J. (2018). Unaddressed privacy risks in accredited health and wellness apps: a cross-sectional systematic assessment. BMC medicine, 16(1), 1-10.
  31. J. C. Denny and B. Malin, 'Protecting patient privacy when sharing patient-level data for research,' in Science Translational Medicine, vol. 8, no. 322, p. 322ra7, 2016.
  32. B. Norgeot, G. Quer, B. K. Beaulieu-Jones, A. Torkamani, and R. Dias, 'Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist,' in Nature Medicine, vol. 25, no. 9, pp. 1313-1317, 2019.
  33. Z. Obermeyer, B. Powers, C. Vogeli, and S. Mullainathan, 'Dissecting racial bias in an algorithm used to manage the health of populations,' in Science, vol. 366, no. 6464, pp. 447-453, 2019.
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