Vol. 3 No. 1 (2023): Journal of AI-Assisted Scientific Discovery
Articles

Machine Learning for Predictive Modelling of Healthcare-Associated Infections

Dr. Ananya Gupta
Director of AI Applications in Healthcare, Indian Institute of Science Bangalore, India
Cover

Published 17-04-2023

Keywords

  • Healthcare-associated infections,
  • Machine learning,
  • Predictive modeling,
  • Hospital-acquired infections

How to Cite

[1]
Dr. Ananya Gupta, “Machine Learning for Predictive Modelling of Healthcare-Associated Infections”, Journal of AI-Assisted Scientific Discovery, vol. 3, no. 1, pp. 21–28, Apr. 2023, Accessed: Nov. 10, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/4

Abstract

Healthcare-associated infections (HAIs) pose a significant threat to patient safety and increase healthcare costs. Predictive modeling using machine learning (ML) techniques offers a promising approach to prevent HAIs. This study develops ML models for predicting and preventing HAIs in hospitals. We utilize a dataset containing patient demographics, clinical variables, and infection outcomes. Various ML algorithms are trained and evaluated for their predictive performance. Our results show that [Insert findings and key results here]. This research contributes to the advancement of predictive modeling for HAIs and underscores the potential of ML in healthcare infection prevention.

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References

  1. Reddy, Byrapu, and Surendranadha Reddy. "Evaluating The Data Analytics For Finance And Insurance Sectors For Industry 4.0." Tuijin Jishu/Journal of Propulsion Technology 44.4 (2023): 3871-3877.
  2. Pulimamidi, Rahul. "Emerging Technological Trends for Enhancing Healthcare Access in Remote Areas." Journal of Science & Technology 2.4 (2021): 53-62.
  3. Venigandla, Kamala, and Venkata Manoj Tatikonda. "Optimizing Clinical Trial Data Management through RPA: A Strategy for Accelerating Medical Research."
  4. Reddy, Surendranadha Reddy Byrapu. "Ethical Considerations in AI and Data Science-Addressing Bias, Privacy, and Fairness." Australian Journal of Machine Learning Research & Applications 2.1 (2022): 1-12.
  5. Sasidharan Pillai, Aravind. “Utilizing Deep Learning in Medical Image Analysis for Enhanced Diagnostic Accuracy and Patient Care: Challenges, Opportunities, and Ethical Implications”. Journal of Deep Learning in Genomic Data Analysis 1.1 (2021): 1-17.
  6. Pulimamidi, Rahul. "Leveraging IoT Devices for Improved Healthcare Accessibility in Remote Areas: An Exploration of Emerging Trends." Internet of Things and Edge Computing Journal 2.1 (2022): 20-30.