Trends and methods in intensive care unit (ICU) research using machine learning: latent dirichlet allocation (LDA)-based thematic literature review.

introductionThe use of machine learning (ML) in intensive care units (ICUs) has led to a large yet fragmented body of literature. It is imperative to conduct a systematic analysis and synthesis of this research to identify methodological trends, clinical applications, and knowledge deficits.

methodsA systematic literature review was conducted in accordance with the PRISMA guidelines, encompassing 2,507 ICU-focused ML publications from 2019 to 2024. Latent Dirichlet Allocation (LDA), an unsupervised topic modeling approach, was used with n-gram and no-n-gram tokenization strategies. Bayesian optimization approaches were used to increase model coherence and diversity.

resultsThe analysis demonstrated a substantial degree of methodological variability, emphasizing the predominance of studies on infection surveillance and complication prediction. N-gram tokenization efficiently identified clinically specific topics, but no-n-gram techniques produced larger interpretative groups. Underexplored fields include emerging research areas like drug response prediction, pediatric-specific modeling, and surgical risk classification.

conclusionIn conclusion, the study highlights the significance of methodological transparency and tokenization strategies while offering a thorough topic overview and identifying methodological trends in the literature on ICU - ML. Future research should prioritize neglected areas such as pediatric care modeling and therapy response, utilizing advanced ML techniques and multimodal data integration to enhance the outcomes of ICU patients.