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Record W2138005877 · doi:10.1109/iske.2008.4731125

A multi-agent prototype system for medical diagnosis

2008· article· en· W2138005877 on OpenAlex
Qiao Yang, John S. Shieh

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMulti-Agent Systems and Negotiation
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsJavaMedical diagnosisComputer scienceExpert systemJADE (particle detector)Multi-agent systemNegotiationDecision support systemArtificial intelligenceSoftware engineeringModel-based reasoningKnowledge representation and reasoningProgramming languageMedicine

Abstract

fetched live from OpenAlex

Coordination and negotiation among agents are necessary when multiple agents are motivated to make a diagnosis for a patient together. In this paper a model of a multi-agent diagnosis helping system (MADHS) is given, where several knowledge-based systems are considered as cooperative agents in medical diagnoses. Fuzziness and uncertainty have been incorporated into decision trees to form the reasoning mechanism of agents. A novel coordination mechanism is then described, which is able to reach the final diagnosis compatible with both patient's anamnesis and existing medical principles. The model and reasoning mechanisms are implemented using Java, Java agent development framework (JADE), Java expert system shell (JESS) and NRC FuzzyJ Toolkit, and is tested by both traditional Chinese and western medical diagnosis examples. It is anticipated that the proposed system and technologies will be widely used in applicative areas, such as multi-agent medical diagnosis, medical helping, and other automatic diagnosis and decision making systems.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.915
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.055
GPT teacher head0.286
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations13
Published2008
Admission routes1
Has abstractyes

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