A fuzzy pattern matching technique for diagnostic ECG classification
Bibliographic record
Abstract
The authors describe a technique for the automatic acquisition of expert knowledge in order to set up a knowledge base for the diagnostic classification of ECG signals. The method is indirect, because the knowledge of the expert, in contrast with the general approach which learns through the direct communication of rules and facts, is derived from a learning set of classified ECGs. It is, on the other hand, different from conventional statistical techniques, because (1) the reference classification is given by experts and not by independent exams like autopsy, coronarography, echocardiography, cardiac surgery, and so on, and (2) this classification can be uncertain, i.e. the various classes are associated with each ECG with certainty factors which can differ from 0 or 1. The data are derived from the CSE pilot diagnostic library. In this preliminary study, the results of the method, which is based on fuzzy pattern matching, show a global type-4 error (complete disagreement) equal to 12.5%.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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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 | 0.000 | 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 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".