Deep Learning Model for Early Subsequent COPD Exacerbation Prediction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Chronic Obstructive Pulmonary Disease (COPD) patients have a burden of frequent exacerbations during daily life. Automatic solutions for early COPD exacerbation prediction could promote COPD healthcare and reduce hospital readmissions. Previous works didn't consider symptoms change patterns which might not be effective for timely and personalized therapy. When using a pulse oximeter for COPD diagnosis, arterial oxygen saturation (SPO2) levels are targeted, depending on whether a patient is stable, hospitalized, or being recovered from exacerbation states. However, the timely management of COPD is a problem, due to the manual monitoring of individual measurements. This research investigates whether the Long Short-Term Memory (LSTM) model can predict early COPD subsequent exacerbation by prompting therapy depending on COPD symptoms patterns and SPO2 burden levels. Time-stamped Electronic Health Record (EHR) from COPD patients' data time series were examined, over subsequent days, with the aim to evaluate a short-time window which a monitoring system for an accurate and early prediction of subsequent exacerbations could be based on. Therefore, the LSTM model was evaluated by varying a window of one to six prior time-steps, to forecast a subsequent day. The window of 1 day showed a good performance of a training accuracy of 87%, a testing accuracy of 85% and an area under the curve (AUC) of 0.83, by employing the training and testing model on only 54 patients.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it