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Paul Lewis

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5 papers
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5

KER Journal 2011 Journal Article

A knowledge-rich distributed decision support framework: a case study for brain tumour diagnosis

  • David Dupplaw
  • Madalina Croitoru
  • Srinandan Dasmahapatra
  • Alex Gibb
  • Horacio González-Vélez
  • Miguel Lurgi
  • Bo Hu
  • Paul Lewis

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.

ICAPS Conference 2011 Conference Paper

Ensemble Monte-Carlo Planning: An Empirical Study

  • Alan Fern
  • Paul Lewis

Monte-Carlo planning algorithms, such as UCT, select actions at each decision epoch by intelligently expanding a single search tree given the available time and then selecting the best root action. Recent work has provided evidence that it can be advantageous to instead construct an ensemble of search trees and to make a decision according to a weighted vote. However, these prior investigations have only considered the application domains of Go and Solitaire and were limited in the scope of ensemble configurations considered. In this paper, we conduct a more exhaustive empirical study of ensemble Monte-Carlo planning using the UCT algorithm in a set of six additional domains. In particular, we evaluate the advantages of a broad set of ensemble configurations in terms of space and time efficiency in both parallel and singlecore models. Our results demonstrate that ensembles are an effective way to improve performance per unit time given a parallel time model and performance per unit space in a single-core model. However, contrary to prior isolated observations, we did not find significant evidence that ensembles improve performance per unit time in a single-core model.

KER Journal 2011 Journal Article

The design and implementation of a novel security model for HealthAgents

  • Liang Xiao
  • Srinandan Dasmahapatra
  • Paul Lewis
  • Bo Hu
  • Andrew Peet
  • Alex Gibb
  • David Dupplaw
  • Madalina Croitoru

Abstract In this paper, we analyze the special security requirements for software support in health care and the HealthAgents system in particular. Our security solution consists of a link-anonymized data scheme, a secure data transportation service, a secure data sharing and collection service, and a more advanced access control mechanism. The novel security service architecture, as part of the integrated system architecture, provides a secure health-care infrastructure for HealthAgents and can be easily adapted for other health-care applications.

KER Journal 2011 Journal Article

The HealthAgents ontology: knowledge representation in a distributed decision support system for brain tumours

  • Bo Hu
  • Madalina Croitoru
  • Roman Roset
  • David Dupplaw
  • Miguel Lurgi
  • Srinandan Dasmahapatra
  • Paul Lewis
  • Juan Martínez-Miranda

Abstract In this paper we present our experience of representing the knowledge behind HealthAgents (HA), a distributed decision support system for brain tumour diagnosis. Our initial motivation came from the distributed nature of the information involved in the system and has been enriched by clinicians’ requirements and data access restrictions. We present in detail the steps we have taken towards building our ontology starting from knowledge acquisition to data access and reasoning. We motivate our representational choices and show our results using domain examples used by clinical partners in HA.

AAAI Conference 2007 Conference Paper

On Capturing Semantics in Ontology Mapping

  • Bo Hu
  • Paul Lewis

Ontology mapping is a complex and necessary task for many Semantic Web (SW) applications. The perspective users are faced with a number of challenges including the difficulties of capturing semantics. In this paper we present a threedimensional ontology mapping model. This model reflects the engineering steps needed to materialise a versatile mapping system in order to meet the demands on semantic interoperability in the SW environment. We solidify the formalisation with specialised algorithms and we analyse their effectiveness and performance by way of benchmark tests.

v2026.09.13