Machine learning can predict patterns of pulmonary function
Bibliographic record
Abstract
Background: Respiratory oscillometry (Osc) is pulmonary function (PF) modality that measures respiratory mechanics; however, interpretation of Osc is challenging. Hypothesis: Machine learning (ML) facilitates Osc interpretation. Objective: To develop a novel ML architecture to classify patterns of PF and compare its accuracy to expert opinion and a physician verified label. Methods: Data were taken from 245 subjects with 1924 valid 10 Hz mono-frequency Osc tests. Full PF tests and clinical data were used for the gold standard label. Osc measurements of flow, volume, and pressure were inputted for ML and randomly partitioned into training and validation (70:30) sets based on unique subject count. The MiniROCKET algorithm was applied to generate features from the input data which were resolved by a ridge regression classifier to different patterns of PF. A soft voting scheme was implemented on the output classifier scores to reach a final prediction for each Osc test. Results were averaged over 10 experimental runs. ML performance was compared to 11 experts in the interpretations of 72 randomly selected oscillograms. Results: Validation accuracy of ML was 86±4% overall, with highest accuracy in identifying normal (N) and restrictive (R) patterns (91±2%). ML was significantly better than expert interpretations of 72 Osc tests for recognizing all but obstructive (O) patterns, with highest accuracy in identifying N patterns (p<0.001). Conclusions: ML can resolve mono-frequency Osc recordings to N, R, and mixed O-R patterns with significantly greater accuracy than experts in the field.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".