Table of Contents

The Use of Machine Learning Decision Tree Algorithms in Phenotyping Acute Respiratory Distress Syndrome (ARDS) Based on Clinical, Radiological, and Biological Heterogeneity- A Review

Published on: 8th July, 2025

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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Digital Model’s Structure Andremote Patient Monitoring in Respiratory Medicine

Published on: 21st July, 2025

Digital regression models based on an interactive questionnaire and objectively measured results were used for the investigation of new objective methods of remote monitoring of respiratory patients. 43 patients with COPD and 26 with bronchial asthma were examined in a retrospective-prospective observation study before and after exacerbation in the hospital (the first observation). After that, theywere monitored by a digital system with an interactive questionnaire including results of Smart Watch use and a velometric test at home for at least 6 months. The effectiveness of remote patient monitoring was achieved by changes in the treatment program and rehabilitation. An integrative scale for patient monitoring effectiveness evaluation was used for a comparison study before and after remote monitoring wasstarted (historical control). The results of correlation, regression analysis, and OR calculation showed that new monitoring parameters: velometric test distance, daily steps count, night sleep duration, and the number of night awake ups were dependent on the dyspnea score and FEV1. The system of remote patient monitoring based on a digital model decreased the number of calls for emergency medical care, hospitalizations, and increased the effectiveness score of patient monitoring.
Cite this ArticleCrossMarkPublonsHarvard Library HOLLISGrowKudosResearchGateBase SearchOAI PMHAcademic MicrosoftScilitSemantic ScholarUniversite de ParisUW LibrariesSJSU King LibrarySJSU King LibraryNUS LibraryMcGillDET KGL BIBLiOTEKJCU DiscoveryUniversidad De LimaWorldCatVU on WorldCat
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