Using upsampling CONV-LSTM with metadata embedding for respiratory sound classification
Bibliographic record
Abstract
Respiratory diseases are one of the leading causes of death around the world and they severely affect patient quality of life. Auscultation is an essential method for diagnosing respiratory diseases, and it is low-cost and convenient. However, auscultation requires experts who are highly experienced. Medical trainees suffer from misdiagnosis inevitably. To address this issue, a novel machine learning model is proposed, which consists of upsampling convolutional neural network (CNN), a long short-term memory network (LSTM), and a fully connected network (FCNN) with embedding layers to classify respiratory sounds into seven categories: Normal (N), Rhonchi (R), Wheeze (W), Stridor (S), Coarse Crackle (CC), Fine Crackle (FC), Wheeze & Crackle (WC). The model is trained and evaluated on the SPRSound dataset and obtained the result on the test dataset with a sensitivity of 0.5716, specificity of 0.7882, average score of 0.6799, harmonic score of 0.6626, and total score of 0.6756.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".