The Use of Machine Learning Decision Tree Algorithms in Phenotyping Acute Respiratory Distress Syndrome (ARDS) Based on Clinical, Radiological, and Biological Heterogeneity-A Review
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Abstract
Background: Acute Respiratory Distress Syndrome (ARDS) is a clinically, radiologically, and biologically heterogeneous condition. This variability contributes to diagnostic challenges and inconsistent responses to therapy. Identifying homogeneous subgroups or phenotypes within ARDS may enhance precision medicine and therapeutic targeting.
Objective: This review evaluates the utility of decision tree–based supervised machine learning (ML) algorithms—specifically CART, Random Forest, and AdaBoost—in phenotyping ARDS using clinical, radiological, and biological data.
Methods: A comprehensive literature search was conducted between December 2023 and March 2024 using PubMed and Google Scholar. Search terms included ‘decision tree in ARDS’, ‘phenotype in ARDS’, and ‘ML in hypo- and hyperinflammatory ARDS’. Twenty-six relevant articles were included, comprising original studies and reviews.
Results: Decision tree–based models have demonstrated significant potential in classifying ARDS subtypes using routine clinical variables, radiographic features, and biomarker profiles. These algorithms have shown strong predictive performance in differentiating inflammatory phenotypes, forecasting mortality, and enabling early ARDS prediction.
Conclusion: Decision tree algorithms offer a promising approach to ARDS phenotyping by leveraging routinely available data. Their interpretability and predictive accuracy may aid in translating complex biological insights into bedside clinical decision-making, advancing personalized care in critical illness.
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