ArticleJournal of the American Medical Informatics Association : JAMIA2026
AI-Techniques Loss-Based Algorithm for Severity Classification (ATLAS): a novel approach for continuous quantification of exertional symptoms during incremental exercise testing.
Article in Journal of the American Medical Informatics Association : JAMIA, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A protocol for validation of novel artificial intelligence-based framework for dyspnoea investigation with cardiopulmonary exercise testing.ERJ open research · 2026Article
- Rethinking normality in cardiopulmonary exercise testing: why percentile-based thresholds deserve a greater role.ERJ open research · 2026Article
- Resting lung volume phenotypes in COPD: implications for exertional dyspnoea and exercise tolerance.ERJ open research · 2025Article
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Authors and funding
11 authors.
Funding
Abstract
objectiveHeightened muscular effort and breathlessness (dyspnea) are disabling sensory experiences. We sought to improve the current approach of assessing these symptoms only at the maximal effort to new paradigms based on their continuous quantification throughout cardiopulmonary exercise testing (CPET). MATERIALS AND
methodsAfter establishing sex- and age-adjusted reference centiles (0-10 Borg scale), we developed a novel algorithm (AI-Techniques Loss-Based Algorithm for Severity Classification [ATLAS]) based on reciprocal exponential loss for CPET data from patients with chronic obstructive lung disease of varied severity.
resultsCategories of dyspnea intensity by ATLAS-but not dyspnea at peak exercise-correctly discriminated patients in progressively higher resting and exercise impairment (P < .05). DISCUSSION: This new AI-techniques approach will be translated to the care of disabled patients to uncover the seeds and consequences of their activity-related symptoms.
conclusionsWe used innovative informatics research to change paradigms in displaying, quantifying, and analyzing effort-related symptoms in patient populations.
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