A Review on the Importance of Machine Learning in the Health-Care Domain

Authors

DOI:

https://doi.org/10.4108/eetpht.10.5330

Keywords:

Machine Learning, Health-Care, Patients, Deep Learning, Natural Language Processing

Abstract

INTRODUCTION: An analysis of the convergence of blockchain and artificial intelligence (AI) technology demonstrates how these technologies can work together to revolutionize data management across a wide range of industries with their synergistic potential.

OBJECTIVES: This paper discusses the integration of blockchain and artificial intelligence, the authors present an innovative framework that takes advantage of their strengths. As a result of blockchain's immutability and transparency, data can be securely stored and shared within this framework, making it ideal for sectors such as healthcare, finance, and supply chain.

METHODS: To begin with, the paper discusses blockchain and artificial intelligence individually, emphasizing their respective advantages in decentralized data storage and intelligent decision-making. Blockchain-AI convergence is inevitable as both deal with data and value.

RESULTS: As a result, the research paper highlights how blockchain and AI technologies can be transformed into transformative technologies.

CONCLUSION: Using the synergistic framework presented in this paper, data management can be made more secure, transparent, and intelligent, with implications that go beyond traditional industries into emerging fields like the Internet of Things (IoT) and smart cities.

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References

Esteva, Andre, Alexandre Robicquet, Bharath Ramsundar, Volodymyr Kuleshov, Mark DePristo, Katherine Chou, Claire Cui, Greg Corrado, Sebastian Thrun, and Jeff Dean. "A guide to deep learning in healthcare." Nature medicine 25, no. 1 (2019): 24-29. DOI: https://doi.org/10.1038/s41591-018-0316-z

Nalchigar, Soroosh, Eric Yu, and Karim Keshavjee. "Modeling machine learning requirements from three perspectives: a case report from the healthcare domain." Requirements Engineering 26 (2021): 237-254. DOI: https://doi.org/10.1007/s00766-020-00343-z

Vellido, Alfredo. "The importance of interpretability and visualization in machine learning for applications in medicine and health care." Neural computing and applications 32, no. 24 (2020): 18069-18083. DOI: https://doi.org/10.1007/s00521-019-04051-w

Cai, Tianrun, Andreas A. Giannopoulos, Sheng Yu, Tatiana Kelil, Beth Ripley, Kanako K. Kumamaru, Frank J. Rybicki, and Dimitrios Mitsouras. "Natural language processing technologies in radiology research and clinical applications." Radiographics 36, no. 1 (2016): 176-191. DOI: https://doi.org/10.1148/rg.2016150080

Zeng, Zexian, Yu Deng, Xiaoyu Li, Tristan Naumann, and Yuan Luo. "Natural language processing for EHR-based computational phenotyping." IEEE/ACM transactions on computational biology and bioinformatics 16, no. 1 (2018): 139-153. DOI: https://doi.org/10.1109/TCBB.2018.2849968

Adege, Abebe Belay, Hsin-Piao Lin, Getaneh Berie Tarekegn, and Shiann-Shiun Jeng. "Applying deep neural network (DNN) for robust indoor localization in multi-building environment." Applied Sciences 8, no. 7 (2018): 1062. DOI: https://doi.org/10.3390/app8071062

Kim, Jin, Nara Shin, Seung Yeon Jo, and Sang Hyun Kim. "Method of intrusion detection using deep neural network." In 2017 IEEE international conference on big data and smart computing (BigComp), pp. 313-316. IEEE, 2017.

Shailaja, K., Banoth Seetharamulu, and M. A. Jabbar. "Machine learning in healthcare: A review." In 2018 Second international conference on electronics, communication and aerospace technology (ICECA), pp. 910-914. IEEE, 2018. DOI: https://doi.org/10.1109/ICECA.2018.8474918

Hathaliya, Jigna, Priyanka Sharma, Sudeep Tanwar, and Rajesh Gupta. "Blockchain-based remote patient monitoring in healthcare 4.0." In 2019 IEEE 9th international conference on advanced computing (IACC), pp. 87-91. IEEE, 2019. DOI: https://doi.org/10.1109/IACC48062.2019.8971593

Agrawal, Shweta, and Sanjiv Kumar Jain. "Medical text and image processing: applications, issues and challenges." Machine Learning with Health Care Perspective: Machine Learning and Healthcare (2020): 237-262. DOI: https://doi.org/10.1007/978-3-030-40850-3_11

Grajales III, Francisco Jose, Samuel Sheps, Kendall Ho, Helen Novak-Lauscher, and Gunther Eysenbach. "Social media: a review and tutorial of applications in medicine and health care." Journal of medical Internet research 16, no. 2 (2014): e2912. DOI: https://doi.org/10.2196/jmir.2912

Istepanian, Robert SH, and Turki Al-Anzi. "m-Health 2.0: new perspectives on mobile health, machine learning and big data analytics." Methods 151 (2018): 34-40. DOI: https://doi.org/10.1016/j.ymeth.2018.05.015

Wang, Haolin, Zhilin Huang, Danfeng Zhang, Johan Arief, Tiewei Lyu, and Jie Tian. "Integrating co-clustering and interpretable machine learning for the prediction of intravenous immunoglobulin resistance in kawasaki disease." IEEE Access 8 (2020): 97064-97071. DOI: https://doi.org/10.1109/ACCESS.2020.2996302

Lalmuanawma, Samuel, Jamal Hussain, and Lalrinfela Chhakchhuak. "Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review." Chaos, Solitons & Fractals 139 (2020): 110059. DOI: https://doi.org/10.1016/j.chaos.2020.110059

Syed, Khajamoinuddin, William Sleeman IV, Kevin Ivey, Michael Hagan, Jatinder Palta, Rishabh Kapoor, and Preetam Ghosh. "Integrated natural language processing and machine learning models for standardizing radiotherapy structure names." In Healthcare, vol. 8, no. 2, p. 120. MDPI, 2020. DOI: https://doi.org/10.3390/healthcare8020120

Suresh, Shruthi, David T. Newton, Thomas H. Everett IV, Guang Lin, and Bradley S. Duerstock. "Feature selection techniques for a machine learning model to detect autonomic dysreflexia." Frontiers in Neuroinformatics 16 (2022): 901428. DOI: https://doi.org/10.3389/fninf.2022.901428

Javaid, Mohd, Abid Haleem, Ravi Pratap Singh, Rajiv Suman, and Shanay Rab. "Significance of machine learning in healthcare: Features, pillars and applications." International Journal of Intelligent Networks 3 (2022): 58-73. DOI: https://doi.org/10.1016/j.ijin.2022.05.002

Faust, Oliver, Yuki Hagiwara, Tan Jen Hong, Oh Shu Lih, and U. Rajendra Acharya. "Deep learning for healthcare applications based on physiological signals: A review." Computer methods and programs in biomedicine 161 (2018): 1-13. DOI: https://doi.org/10.1016/j.cmpb.2018.04.005

Kaur, Prableen, Manik Sharma, and Mamta Mittal. "Big data and machine learning based secure healthcare framework." Procedia computer science 132 (2018): 1049-1059. DOI: https://doi.org/10.1016/j.procs.2018.05.020

Bini, Stefano A. "Artificial intelligence, machine learning, deep learning, and cognitive computing: what do these terms mean and how will they impact health care?." The Journal of arthroplasty 33, no. 8 (2018): 2358-2361. DOI: https://doi.org/10.1016/j.arth.2018.02.067

Supriya, M., and A. J. Deepa. "Machine learning approach on healthcare big data: a review." Big Data and Information Analytics 5, no. 1 (2020): 58-75. DOI: https://doi.org/10.3934/bdia.2020005

Leonardi, Rosalia, Antonino Lo Giudice, Gaetano Isola, and Concetto Spampinato. "Deep learning and computer vision: two promising pillars, powering the future in orthodontics." In Seminars in Orthodontics, vol. 27, no. 2, pp. 62-68. WB Saunders, 2021. DOI: https://doi.org/10.1053/j.sodo.2021.05.002

Solfa, Federico Del Giorgio, and Fernando Rogelio Simonato. "Big Data Analytics in Healthcare: Exploring the Role of Machine Learning in Predicting Patient Outcomes and Improving Healthcare Delivery." International Journal of Computations, Information and Manufacturing (IJCIM) 3, no. 1 (2023): 1-9. DOI: https://doi.org/10.54489/ijcim.v3i1.235

Sun, Yuwei, Hideya Ochiai, and Hiroshi Esaki. "Decentralized deep learning for multi-access edge computing: A survey on communication efficiency and trustworthiness." IEEE Transactions on Artificial Intelligence 3, no. 6 (2021): 963-972. DOI: https://doi.org/10.1109/TAI.2021.3133819

Cai, Lei, Jingyang Gao, and Di Zhao. "A review of the application of deep learning in medical image classification and segmentation." Annals of translational medicine 8, no. 11 (2020). DOI: https://doi.org/10.21037/atm.2020.02.44

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Published

07-03-2024

How to Cite

1.
Bhatia TK, Prerana, Singh S, Saluja N, Gour YS. A Review on the Importance of Machine Learning in the Health-Care Domain . EAI Endorsed Trans Perv Health Tech [Internet]. 2024 Mar. 7 [cited 2024 Nov. 15];10. Available from: https://publications.eai.eu/index.php/phat/article/view/5330