Predetection of Stroke by Using Heuristics and Artificial Neural Networks
Bibliographic record
Abstract
The strokes are an important cause of death for all the people, not only for the aged population. Sooner a stroke is discovered better chances are for the patient to minimize the damage or even to survive it. The complexity of strokes reveals clearly the importance of early stroke predetection which are not only helping the doctors but they could literally save lives. Algorithms for predetection of stroke are diverse, however they are little explored. This thesis is mainly centered on predetection of stroke, based on the inversion of T waves in electrocardiograms. Two models were proposed in this thesis to help providing efficient predetection of stroke for people suffering of myocardium diseases and myocardial ischemia. The algorithms were tested on data from four electrocardiograms given by a library and five electrocardiograms from five different patients. Filters for noisy signals are also provided in this thesis. These algorithms can be used as a tool by nurses and doctors but they do not represent a fully automated detection of stroke.
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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 | 0.001 |
| 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".