Key result
Novel clustering-based machine learning achieves ~83% accuracy for automatic pain assessment in ICU patients.
Why the study?
The role of machine learning methods to evaluate pain levels in clinical settings remained unclear, representing an unmet need for assisted tools.
Does a congeneric clustering-based machine learning approach improve automatic pain level assessment in ICU patients compared to existing methods?
Observational
Does a congeneric clustering-based machine learning approach improve automatic pain level assessment in ICU patients compared to existing methods?
Answers are grounded in this paper and cite their sources.
A novel congeneric clustering-based machine learning approach achieved 82.86% accuracy in assessing pain levels of ICU patients using physiologic measures.
Recent studies showed that machine learning can assist to better evaluate the induced pain level(s) on healthy individuals in a controlled environment. However, the role of these methods in clinical settings remained unclear and there is an unmet need to develop machine learning assisted tools in pain. The aim of this paper is to develop an automatic pain level assessment model based on patients' physiologic measures from the Medical Information Mart for Intensive Care (MIMIC III). There were two study phases; 1) pilot study, tested three existing machine learning methods proposed recently for healthy individuals. However, these yield poor performances in MIMIC III patients. 2) group study, a novel congeneric clustering method which divided patients into eleven categories and trained a dedicated model for each one. The clustering effectiveness of the proposed congeneric clustering method by showing the highest classification accuracy of 82.86%.
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Fang et al. (2022) conducted an observational in Pain in Intensive Care Unit patients. Congeneric clustering-based machine learning method vs. Three existing machine learning methods was evaluated on Classification accuracy of pain level assessment. A novel congeneric clustering-based machine learning method achieved a classification accuracy of 82.86% for automatic pain level assessment in intensive care unit patients.
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