Automated modeling of cardiac electrical activity
Bibliographic record
Abstract
The cardiac action potential (AP) differs significantly from cell to cell. The understanding of this variation is necessary to explain normal and pathological cardiac function. Existing mathematical models reproduce typical action potentials but do not represent all measured action potentials. We have developed a genetic algorithm (GA) to obtain conductance parameters to model arbitrary APs. Our method uses Nygren's human atrial cell model as a base. Initially we implemented several schemes of GAs and finally we developed a custom-GA that is optimized for the atrial cell action potential modeling. Our custom-GA converges to the best parameter set within 80 iterations and always keeps the best one. It also ensures that the new parameters are within the specified search range. Using this algorithm we were able to obtain the conductance values for the published Nygren model with a maximum error of 0.03%. In addition, this algorithm successfully calculated the conductance values of an arbitrary action potential waveform. Our results suggest that the conductance parameters to reproduce any desired atrial action potential can be computed using this algorithm.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".