Vol. 3 No. 2 (2023): Journal of Deep Learning in Genomic Data Analysis
Articles

Real-Time AI-Powered Systems for Enhancing Hospital Infection Control: Utilizing Machine Learning to Monitor and Manage Infection Risks and Outbreaks

Dr. Ingrid Gustavsson
Associate Professor of Human-Computer Interaction, University of Gothenburg, Sweden
Cover

Published 13-12-2023

Keywords

  • Hospital Infection Control,
  • Infection Risks and Outbreaks

How to Cite

[1]
Dr. Ingrid Gustavsson, “Real-Time AI-Powered Systems for Enhancing Hospital Infection Control: Utilizing Machine Learning to Monitor and Manage Infection Risks and Outbreaks”, Journal of Deep Learning in Genomic Data Analysis, vol. 3, no. 2, pp. 87–100, Dec. 2023, Accessed: Nov. 13, 2024. [Online]. Available: https://thelifescience.org/index.php/jdlgda/article/view/52

Abstract

Hospitals are some of the main hotspots for disease-causing microorganisms and need to be able to quickly and effectively respond to managing infection risks and outbreaks. Technological interventions can often lead to radical and transformative changes in the functioning of social systems, and hospital surveillance and infection control is no exception. In particular, the application of machine learning has gradually begun to revolutionize the infection control domain. The objective of this survey text is to discuss how advanced artificial intelligence-powered solutions can be used to monitor, model, and manage hospital-associated infections. The easy and ready availability of healthcare data, combined with sensor-to-cloud communication and analytic services, rolls out a new paradigm for deploying such interventions in real life. A healthcare facility is one of the principal hotspots of contagious diseases. The rise in infection rates, despite investment in healthcare infrastructure around the world, is raising questions about the efficacy of the disease control measures put in place. Consequently, managing such risks has become an important component of healthcare provision services. The importance of timely surveillance data is especially critical in healthcare response, thereby making quicker the personalization of outbreak surgical decision-making. Emerging technologies involve appealing to infectious disease modeling techniques and predictive analytics. Despite the potential of these technologies, no systematic survey discussing the utilization of advanced algorithms exists to handle hospital systems and disease interaction. In this work, we bring forward the potential laid by these technologies and introduce the reader to the multitude of uses. Infections have posed a significant rise in the aging and vulnerable population, especially resulting from problems of antibiotic resistance. Public health strategies to reduce the risk of such infections are numerous and could be coordinated by public health authorities, local health jurisdictions, and other healthcare networks.

Downloads

Download data is not yet available.

References

  1. Prabhod, Kummaragunta Joel. "Deep Learning Models for Predictive Maintenance in Healthcare Equipment." Asian Journal of Multidisciplinary Research & Review 1.2 (2020): 170-214.
  2. Pushadapu, Navajeevan. "AI and Seamless Data Flow to Health Information Exchanges (HIE): Advanced Techniques and Real-World Applications." Journal of Machine Learning in Pharmaceutical Research 2.1 (2022): 10-55.
  3. Bao, Y.; Qiao, Y.; Choi, J.E.; Zhang, Y.; Mannan, R.; Cheng, C.; He, T.; Zheng, Y.; Yu, J.; Gondal, M.; et al. Targeting the lipid kinase PIKfyve upregulates surface expression of MHC class I to augment cancer immunotherapy. Proc. Natl. Acad. Sci. USA 2023, 120, e2314416120.
  4. Gayam, Swaroop Reddy. "AI for Supply Chain Visibility in E-Commerce: Techniques for Real-Time Tracking, Inventory Management, and Demand Forecasting." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 218-251.
  5. Nimmagadda, Venkata Siva Prakash. "AI-Powered Risk Management and Mitigation Strategies in Finance: Advanced Models, Techniques, and Real-World Applications." Journal of Science & Technology 1.1 (2020): 338-383.
  6. Putha, Sudharshan. "AI-Driven Metabolomics: Uncovering Metabolic Pathways and Biomarkers for Disease Diagnosis and Treatment." Distributed Learning and Broad Applications in Scientific Research 6 (2020): 354-391.
  7. Sahu, Mohit Kumar. "Machine Learning Algorithms for Enhancing Supplier Relationship Management in Retail: Techniques, Tools, and Real-World Case Studies." Distributed Learning and Broad Applications in Scientific Research 6 (2020): 227-271.
  8. Kasaraneni, Bhavani Prasad. "Advanced Machine Learning Algorithms for Loss Prediction in Property Insurance: Techniques and Real-World Applications." Journal of Science & Technology 1.1 (2020): 553-597.
  9. Kondapaka, Krishna Kanth. "Advanced AI Techniques for Optimizing Claims Management in Insurance: Models, Applications, and Real-World Case Studies." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 637-668.
  10. Kasaraneni, Ramana Kumar. "AI-Enhanced Cybersecurity in Smart Manufacturing: Protecting Industrial Control Systems from Cyber Threats." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 747-784.
  11. Pattyam, Sandeep Pushyamitra. "AI in Data Science for Healthcare: Advanced Techniques for Disease Prediction, Treatment Optimization, and Patient Management." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 417-455.
  12. Kuna, Siva Sarana. "AI-Powered Techniques for Claims Triage in Property Insurance: Models, Tools, and Real-World Applications." Australian Journal of Machine Learning Research & Applications 1.1 (2021): 208-245.
  13. Nimmagadda, Venkata Siva Prakash. "Artificial Intelligence for Automated Loan Underwriting in Banking: Advanced Models, Techniques, and Real-World Applications." Journal of Artificial Intelligence Research and Applications 2.1 (2022): 174-218.
  14. Prabhod, Kummaragunta Joel. "Leveraging Generative AI for Personalized Medicine: Applications in Drug Discovery and Development." Journal of AI-Assisted Scientific Discovery 3.1 (2023): 392-434.
  15. Pushadapu, Navajeevan. "AI-Enhanced Techniques for Pattern Recognition in Radiology Imaging: Applications, Models, and Case Studies." Journal of Bioinformatics and Artificial Intelligence 2.1 (2022): 153-190.
  16. Gayam, Swaroop Reddy. "AI-Driven Customer Support in E-Commerce: Advanced Techniques for Chatbots, Virtual Assistants, and Sentiment Analysis." Distributed Learning and Broad Applications in Scientific Research 6 (2020): 92-123.
  17. Nimmagadda, Venkata Siva Prakash. "Artificial Intelligence and Blockchain Integration for Enhanced Security in Insurance: Techniques, Models, and Real-World Applications." African Journal of Artificial Intelligence and Sustainable Development 1.2 (2021): 187-224.
  18. Putha, Sudharshan. "AI-Driven Molecular Docking Simulations: Enhancing the Precision of Drug-Target Interactions in Computational Chemistry." African Journal of Artificial Intelligence and Sustainable Development 1.2 (2021): 260-300.
  19. Sahu, Mohit Kumar. "Machine Learning for Anti-Money Laundering (AML) in Banking: Advanced Techniques, Models, and Real-World Case Studies." Journal of Science & Technology 1.1 (2020): 384-424.
  20. Kasaraneni, Bhavani Prasad. "Advanced Artificial Intelligence Techniques for Predictive Analytics in Life Insurance: Enhancing Risk Assessment and Pricing Accuracy." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 547-588.
  21. Kondapaka, Krishna Kanth. "Advanced AI Techniques for Retail Supply Chain Sustainability: Models, Applications, and Real-World Case Studies." Journal of Science & Technology 1.1 (2020): 636-669.
  22. Kasaraneni, Ramana Kumar. "AI-Enhanced Energy Management Systems for Electric Vehicles: Optimizing Battery Performance and Longevity." Journal of Science & Technology 1.1 (2020): 670-708.
  23. Pattyam, Sandeep Pushyamitra. "AI in Data Science for Predictive Analytics: Techniques for Model Development, Validation, and Deployment." Journal of Science & Technology 1.1 (2020): 511-552.
  24. Kuna, Siva Sarana. "AI-Powered Solutions for Automated Underwriting in Auto Insurance: Techniques, Tools, and Best Practices." Journal of Science & Technology 1.1 (2020): 597-636.