Classification of Respiratory Physiology with Airwave Oscillometry using Multi-layer Perceptron
Bibliographic record
Abstract
Airwave Oscillometry(OSc) is a novel pulmonary function test with the advantages of effort independent operation and high sensitivity to heterogeneous lungs over conventional tests. However, OSc results are high dimensional without well-established interpretation guideline, posing challenge for clinician’s understanding and limiting its clinical utility. While recent advances in Multi-layer Perceptron (MLP), a machine learning model that has proven capability of complex pattern recognition and classification, this thesis proposed to automate OSc output classification using MLP. We have compared the performance of MLP classifying OSc patterns with different number of hidden layers, investigated the effects of training sample size and input selection on the performance of the classification. The classification performance varied with training-validation division and output categories. We found that MLP with hidden layers outperformed MLP without hidden layer and the highest validation accuracy was 82% for binary classification of respiratory physiology with OSc data using a 2-hidden-layer MLP.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".