Multi-Agent System for Post-Stroke Medical Monitoring in Web-Based Platform
Bibliographic record
Abstract
Stroke is an injury to blood vessels in the brain and affects their cells; this causes the person to lose the functionality of their body and autonomy. The rehabilitation involves a set of activities for the medical specialists, that is, to have a strict control in the care of the patient, which includes the diets, therapies and ingestion of drugs so that the recovery of the patient is carried out in an optimal way and can be reintegrated into their family, social and work environment. This means that achieving communication and coordination among the members of the health area represents a problem; there is no established structured control plan and physicians need to modify them to adjust to the new situation, which is in accordance with the patient clinical needs. Thus, finding a solution to this problem becomes extremely important and, in this work, we propose a multi-agent system for post-stroke monitoring aimed at medical specialists. The main objective of this research is to communicate and coordinate the follow-up of the patient for the reduction of the damages caused by the stroke using the theory of artificial intelligence agents. The results of this research include the multi-agent system in web-based platform, description of the agents and finally, the implementation of the web system oriented to medical specialists.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".