Abstract 15813: Machine Learning Improves Prediction of Heart Failure and Cardiovascular Death Through a Signal Analytical Approach of Echocardiography
Bibliographic record
Abstract
Introduction: Recently, speckle tracking echocardiography (STE) and tissue Doppler imaging (TDI) have gained increasing traction as non-invasive tissue characterization methods within cardiology. But until now many patterns from the strain and TDI curves remain uninvestigated. Signal analytical methods like wavelet analysis have shown promising potential in effectively identifying previously unknown pathological patterns present in ECG signals. Therefore, we hypothesized that a signal analytic approach combined with supervised machine learning (ML) on strain and TDI curves could identify unknown pathophysiological deformation drivers of heart failure (HF) and cardiovascular death (CV death) in the general population. Methods: The analysis included 3781 subjects from the general population. In total 720 novel statistical parameters and wavelet signal parameters from 18 strain curves and 6 TDI curves were generated. The parameters were used to train an ensemble decision tree with 20-fold cross-validation and compared to a baseline ML model trained on 22 conventional echocardiographic parameters. Results: Follow up-time was four years. In total 108 subjects (2.9%) met the outcome. By including statistical and wavelet-derived parameters along with 22 conventional echocardiographic parameters in a combined model, the ML model was significantly improved compared to the baseline model (AUC for conventional model: 0.76 vs combined model: 0.825, p = 0.0086). Both statistical parameters, such as the mean and skewness of the TDI curve, as well as wavelet parameters, such as mean on the first decomposition level of the strain curve, were found to be important in the model. Conclusion Adding novel echocardiographic parameters based on signal analytical statistics and wavelet decomposition methods to preexisting conventional echocardiographic parameters significantly improved the prediction of incident HF or CV death in ML models.
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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 | 0.004 | 0.007 |
| 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) | 0.002 | 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".