ECG Knowledge Discovery Based on Ontologies and Rules Learning for the Support of Personalized Medical Decision Making
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The electrocardiogram (ECG) is the most common non-invasive used method for monitoring the heart condition. Combined with other available patient medical data, the manual analysis and evaluation of ECG data become labour intensive and prone to errors. With the increased amount of available digital medical data, there is a need for proper methods to support medical practitioners in the decision-making process. These practitioners base their diagnostic decisions on standardized procedures, combined with field experience. In this paper, we present the conceptual design for an approach to knowledge discovery of ECG data based on ontologies and rules learning using Learning Classifier Systems (LCS). Ontologies can provide a platform and application-independent representation of knowledge based on the patient's medical data. Furthermore, rule-based reasoning provides a mechanism for discovering new knowledge. LCS provide a tool for automatically discovering new rules that are maximally general and can support sequential decision-making process. The use of LCS and rule-based reasoning provide a mechanism for encoding existing and new knowledge that can improve the efficiency of personalized medical treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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