A Knowledge-Rich Distributed Decision Support Framework: A Case Study for Brain Tumour Diagnosis - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Journal Articles Knowledge Engineering Review Year : 2011

A Knowledge-Rich Distributed Decision Support Framework: A Case Study for Brain Tumour Diagnosis

Abstract

The HealthAgents project aims to provide a decision support system for brain tumour diagnosis using a collaborative network of distributed agents. The goal is that through the aggregation of the small data sets available at individual hospitals, much better decision support classifiers can be created and made available to the hospitals taking part. In this paper, we describe the technicalities of the HealthAgents framework, in particular how the interoperability of the various agents is managed using semantic web technologies. On the broad scale the architecture is based around distributed data-mart agents that provide ontological access to hospitals’ underlying data that has been anonymized and processed from proprietary formats into a canonical format. Classifier producers have agents that gather the global data from participating hospitals such that classifiers can be created and deployed as agents. The design on a microscale has each agent built upon a generic-layered framework that provides the common agent program code, allowing rapid development of agents for the system. We believe that our framework provides a well-engineered, agent-based approach to data sharing in a medical context. It can provide a better basis on which to investigate the effectiveness of new classification techniques for brain tumour diagnosis.

Dates and versions

lirmm-00618606 , version 1 (02-09-2011)

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David Dupplaw, Madalina Croitoru, Srinandan Dasmahapatra, Alex Gibbs, Horacio Gonzales-Velez, et al.. A Knowledge-Rich Distributed Decision Support Framework: A Case Study for Brain Tumour Diagnosis. Knowledge Engineering Review, 2011, 26 (3), pp.247-260. ⟨10.1017/S0269888911000105⟩. ⟨lirmm-00618606⟩
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