Automated Techniques for Detecting Healthcare
Associated Infections: A Review

Automated Techniques for Detecting Healthcare
Associated Infections: A Review

Healthcare-Associated Infections; Machine
Learning; Deep Learning; Natural Language Processing;
Transformer; Electronic Health Records

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

Joseph Karuri Mwaura

https://ijcit.com/index.php/ijcit/article/view/540

School of Pure and Applied Sciences
Kirinyaga University, Kenya
Email: karurimwaura [AT] gmail.com

september 2025

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