Late Breaking Abstract - Detection of small airway obstruction patterns in forced oscillometry(FO) and spirometry using artificial neural networks
Bibliographic record
Abstract
Background: FO is novel and more sensitive in assessing small airways than spirometry. However, FO test interpretation guidelines have not been established, limiting the utility of FO for diagnoses. Multi-layer perceptron(MLP) is a class of artificial neural networks which is capable of categorizing data based on input features using nonlinear operators. Objectives: To construct and evaluate an effective MLP model for detecting and classifying small airway obstruction patterns in FO and spirometry results. Method: The samples used are paired FO-spirometry tests of 200 individuals collected at the PFT Laboratory, UHN. The preliminary experiment involves the first 222 tests of 82 subjects(38F:44M, age=55.4±12.9yrs). Each test is labelled as airflow obstruction(AO) or not according to ATS guidelines, and randomly divided into training, validation, and test datasets at 70:15:15. MLP models with different numbers of inputs, hidden layers, and hidden units are evaluated regarding their performance in classifying AO(positive/negative). Result: The best result is obtained by the MLP model with 44 input features, including 4 biometrics (age, gender, height, weight), 36 FO parameters (R, X, Rin-ex, Xin-ex, each at 9 different frequencies), 4 spirometric parameters (FEV1, FVC, FEF50, FEF75), 1 hidden layers with 140 units and a logistic activation function. By training 200 epochs at 0.001 learning rate, the average validation accuracy is 83% for unseen patterns. Conclusion: MLP is an effective tool for identifying AO patterns in FO and spirometry. The classification accuracy is expected to improve as the training sample size expands and the model parameters are further regulated.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 0.002 |
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".