MLOps in health care: Enhancing Operational Excellence and Improving Patient Outcomes

Muskan Chopra; Kush

MLOps in health care: Enhancing Operational Excellence and Improving Patient Outcomes

June 2023

Vol-9, Issue-3

Paper ID: 20812

ISSN: 2395-4396

Downloads: 0

Abstract & Details

Research Area

Computer Engineering

Keywords

MLOps (Machine Learning Operations) Medical industry Health care delivery ML models (Machine Learning models) Data management Model deployment Regulatory standards Ethical considerations Patient outcomes Resource allocation Predictive analytics Algorithm development Real-time monitoring Data privacy Clinical decision support Health care providers Performance metrics Work flow optimization Quality improvement Risk assessment

Abstract

This paper explores the role of MLOps (Machine Learning Operations) in the medical industry and its impact on health care delivery. It emphasizes the need for a robust operational framework to successfully deploy and manage ML models in real-world health care settings. MLOps provides a systematic approach to streamline the development, deployment, monitoring, and maintenance of ML models, ensuring their reliability, scalability, and compliance with regulatory standards. The paper discusses the challenges faced by health care organizations in implementing ML models and how MLOps addresses these challenges. It also examines key components of an MLOps framework, such as data management, model versioning, reproducibility, and model performance monitoring. Case studies are presented to demonstrate the benefits of MLOps in improving patient outcomes, optimizing resource allocation, and reducing health care costs. Ethical considerations and potential risks associated with MLOps in the medical domain are discussed, emphasizing the importance of responsible and transparent deployment of ML models. Overall, the paper provides a comprehensive overview of MLOps in the medical industry and its trans-formative potential for enhancing health care delivery.

License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author Information

#	Name	Institute / Affiliation
1	Muskan Chopra	Global Institute Of Technology - [GIT], Jaipur, Rajasthan, India
2	Kush	Global Institute Of Technology - [GIT], Jaipur, Rajasthan, India

How to Cite

Use the following formats to cite this article in your research.

APA Style

Chopra, Muskan & Kush (2023). MLOps in health care: Enhancing Operational Excellence and Improving Patient Outcomes. International Journal of Advance Research and Innovative Ideas In Education, 9(3), 4078-4083.

MLA Style

Chopra, Muskan, and Kush. "MLOps in health care: Enhancing Operational Excellence and Improving Patient Outcomes." International Journal of Advance Research and Innovative Ideas In Education, vol. 9, no. 3, 2023, pp. 4078-4083.

IEEE Style

Muskan Chopra and Kush, "MLOps in health care: Enhancing Operational Excellence and Improving Patient Outcomes," International Journal of Advance Research and Innovative Ideas In Education, vol. 9, no. 3, pp. 4078-4083, 2023.

Vancouver Style

Chopra Muskan, Kush. MLOps in health care: Enhancing Operational Excellence and Improving Patient Outcomes. International Journal of Advance Research and Innovative Ideas In Education. 2023;9(3):4078-4083.

Harvard Style

Chopra, Muskan & Kush (2023) 'MLOps in health care: Enhancing Operational Excellence and Improving Patient Outcomes', International Journal of Advance Research and Innovative Ideas In Education, 9(3), pp. 4078-4083.

Chicago Style

Chopra, Muskan and Kush. "MLOps in health care: Enhancing Operational Excellence and Improving Patient Outcomes." International Journal of Advance Research and Innovative Ideas In Education 9, no. 3 (2023): 4078-4083.

Turabian Style

Chopra, Muskan and Kush. "MLOps in health care: Enhancing Operational Excellence and Improving Patient Outcomes." International Journal of Advance Research and Innovative Ideas In Education 9, no. 3 (2023): 4078-4083.