Brain-Inspired Intelligence for Real-Time Health Situation Understanding in Smart e-Health Home Applications
Bibliographic record
Abstract
The autonomic computing layer of the smart e-Health home based on a cognitive dynamic system (CDS) can be a solution for improving health situation understanding, reducing the healthcare system costs, and improving people's quality of life. It can also be a solution for reducing the large number of sudden deaths outside of a hospital due to fatal diseases such as Arrhythmia. Towards this objective, we start from understanding the health situation, by diagnosing healthy and unhealthy persons. For this, we developed a decision-making system that is inspired by the medical doctors (MDs) decision-making processes. Our system is based on a CDS for cognitive decision-making and it can create a decision-making tree automatically. The simple, low complexity algorithmic design of the proposed system makes it suitable for real-time applications. A proof-of-concept case study of the implementation of the CDS was done on Arrhythmia disease. An accuracy of 95.4% was achieved using the proposed algorithms. Also, these algorithms can make a decision in less than 80 ms, and for one User, this includes the time for training. The proposed platform can be extended for more healthcare applications such as screening, disease class diagnosis, prevention, treatment, or monitoring healing. As a result, the proposed CDS algorithms can be an example of the first step for designing the autonomic computing layer of a smart e-Health home platform.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".