Automatic Monitoring System for Artificial Hearts Using Self Organizing Map
Why this work is in the frame
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Bibliographic record
Abstract
This study presents an automatic monitoring system for artificial hearts. The self organizing map (SOM) was applied to monitoring and analysis of an aortic pressure (AoP) signal measured from an adult goat equipped with a total artificial heart. In the proposed system, two different SOMs were used to detect and classify abnormalities in the measured AoP signal. In the first stage, an ordinary SOM, taught with only normal AoP data, was used for detection of abnormalities on the basis of the quantization error in the real-time monitoring task. In the second stage, a supervised SOM was used for classification of abnormalities. The supervised SOM can be regarded as an ordinary SOM with an extra class vector for solving the classification problem. The class vector is assigned to every node in the second SOM as an output weight learned according to Kohonen's learning rule. The effectiveness of detection and classification of abnormalities using these two SOMs was confirmed.
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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