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Introduction <p>The 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.</p> Methods <p>A 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.</p> Results <p>The 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.</p> Conclusion <p>In 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月29日

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  • Duygu Topaloğlu,
  • Olcay Polat

摘要

Introduction

The 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.

Methods

A 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.

Results

The 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.

Conclusion

In 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.