Automated Techniques for Detecting Healthcare Associated Infections: A Review
DOI:
https://doi.org/10.24203/4nd7vf70Keywords:
Healthcare-Associated Infections, Machine Learning, Deep Learning, Natural Language Processing, Transformer, Electronic Health RecordsAbstract
Automated detection of Healthcare-Associated Infections (HAIs) faces major obstacles due to unclear medical documentation, scarcity of well-annotated data, and multiple symptoms that overlap between HAIs. This review investigates recent advances in using classical machine learning, deep learning, transformers, and natural language processing (NLP) methods in detecting healthcare-associated infections. It examines empirical studies from 2019 to 2025, focusing on models' performance based on various metrics, data issues, and ethical considerations. The study sought to assess and compare the performance of natural language processing (NLP) approaches of detecting Healthcare-Associated Infections (HAIs). Ethical and technical concerns such as data privacy and data imbalance, are critical barriers to implementation of NLP to detection of HAIs. The review underscores the promise of NLP to detection of HAIs while emphasizing the need for standardized metrics for evaluating HAI detection model and ethical frameworks of handling the datasets.
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Copyright (c) 2025 Joseph Karuri Mwaura, Kennedy Malanga Ndenga, James Mwikya Reuben
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The articles published in International Journal of Computer and Information Technology (IJCIT) is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
