Virtual Medical Board: A distributed Bayesian agent based approach
Bibliographic record
Abstract
Distributed Decision Making has become of increasing importance to get solution of different real life problems, where decision makers are in geographically dispersed locations. Application of agent and multi agent system in this Distributed Decision Support System is an evolving paradigm. One of such real life problem is medical diagnosis. For critical medical diagnosis, medical board is formed which is a coordinative discussion mechanism between a group of expert physicians to diagnose a patient. But always forming a medical board with a group of expert physicians may not be possible due to lack of infrastructure, availability, time etc. In that situation the role of multi agent based distributed decision making can comes into play. In this paper we develop a Virtual Medical Board System in which a number of software agents (expert agents) act as a group of expert physician with knowledge base(KB), reasoning capability. They coordinatively discuss with each other to diagnose a patieh each other to diagnose a patient. We represent the discussion module of the system in the form of Bayesian Network of Bayesian Agent (BNBA). In BNBA each BA is the expert software agent whose Knowledge Base (KB) is represented in the form of Bayesian Network (BN). Also the BDI (Belief, Desire, Intention) model of each BA is represented in this paper.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".