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Record W2097473336 · doi:10.5555/2433508.2433794

Integrated agent-oriented modeling and simulation of population and healthcare delivery network: application to COPD chronic disease in a Canadian region

2010· article· en· W2097473336 on OpenAlexaffabout
Moez Charfeddine, Benoît Montreuil

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
Topicdemographic modeling and climate adaptation
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPopulationHealth careComputer scienceHealthcare deliveryCOPDPulmonary diseaseDiseaseDistributed computingBusinessMedicineEnvironmental healthEconomics

Abstract

fetched live from OpenAlex

In this paper we introduce a framework for integrated agent-oriented modeling and simulation of the population with a specific chronic disease in a large region and of the network providing relevant healthcare services for this population. We illustrate the framework through the Chronic Obstructive Pulmonary Disease (COPD) population and healthcare delivery network in ��������������������ion of Canada. In this framework exploiting agent oriented modeling, demand for healthcare is expressed deeply through the stochastic modeling of health status evolution of each person in a population of potential patients, where the implications of this evolution generate the demand in terms of patient needs for healthcare and their frequency. In parallel, the organization and functioning of the healthcare delivery network is modeled with an adequate detail level. This is made possible by exploiting the richness of the agent paradigm and by introducing integration mechanisms binding the two model components. 1

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.063
GPT teacher head0.350
Teacher spread0.287 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2010
Admission routes2
Has abstractyes

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