A Machine Learning Model to Detect Small Airway Narrowing Due to Exercise in Overweight and Obese Adults with Asthma
Bibliographic record
Abstract
Regular physical exercise can improve lung function and asthma control, especially in individuals with asthma who are overweight or obese. Nevertheless, being active or exercising could be a risk factor for asthma exacerbations. Monitoring physiological signals during exercise may help to detect potential worsening in asthma and could be used to help persons with asthma to adjust their exercise. Our goal was to determine whether small airway narrowing due to exercise could be predicted using conveniently recorded data including participant’s demographics and respiratory-related signals. In this study, 10 adults with asthma and body mass index (BMI) over 25 kg/m2were asked to cycle for 10 minutes in a room at 20°C. During exercise, the respiratory signals were recorded using inductance plethysmography on the chest and abdomen. Before and after exercise, small airway narrowing was assessed based on respiratory impedance measured by forced oscillation technique. From the chest respiratory signal and demographic data, a total of four features have been obtained in the time domain. To detect airway narrowing, five different machine learning classifiers were fine-tuned and evaluated using a leave-one-subject-out cross-validation approach. The best model had an accuracy of 77.01%, a recall of 82.69%, a specificity of 68.57%, a precision of 79.63%, and an F1 score of 81.13%. These results provide proof of concept that technologies with embedded respiratory signal monitoring may be able to predict airway narrowing during exercise in individuals with asthma, particularly in the overweight or obese population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".