Published 17-04-2023
Keywords
- Healthcare-associated infections,
- Machine learning,
- Predictive modeling,
- Hospital-acquired infections
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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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
- 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.
- Pulimamidi, Rahul. "Emerging Technological Trends for Enhancing Healthcare Access in Remote Areas." Journal of Science & Technology 2.4 (2021): 53-62.
- Venigandla, Kamala, and Venkata Manoj Tatikonda. "Optimizing Clinical Trial Data Management through RPA: A Strategy for Accelerating Medical Research."
- 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.
- 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.
- 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.