In this paper, we describe the unique security issues involved in healthcare domains. These have been addressed to the needs of the HealthAgents project. In the proposed approach, several levels of security have been provided in accordance with Software Engineering principles, ethical regulations for healthcare data, as well as the security requirements usually raised from the distributed clinical settings. The result is the production of a secure and maintainable Multi-Agent System that enables secure communication, uniform home site authentication, and customised resource access authorisation. A security policy rule scheme has been designed for agent interaction modelling. This separates the functional and non-functional (security) requirements but let security policy constraints integrate into the running of the agents via a unified role notion. Each user/agent can play a function role only when its assigned social rights roles permit the access to resources of various types and geographical locations, as specified in the function role behaviour. The approach is illustrated using a comprehensive secure access case.
This paper focuses on the problem of representing, in a meaningful way, the knowledge involved in the HealthAgents project. Our work is motivated by the complexity of representing Electronic Healthcare Records in a consistent manner. We present HADOM (HealthAgents Domain Ontology) which conceptualises the required HealthAgents information and propose describing the sources knowledge by the means of Conceptual Graphs (CGs). This allows to build upon the existing ontology permit-ting for modularity and flexibility. The novelty of our approach lies in the ease with which CGs can be placed above other formalisms and their potential for optimised querying and retrieval.
There are numerous methods for solving the inverse kinematic equations for a robotic arm. This paper proposes a novel, adaptive approach based on multiagent systems (MASs). An MAS employs a distributed, decentralized approach to problem solving that is not commonly employed in conventional robotic arm control. The MAS uses patterns abstracted from various configurations of the robotic arm to provide a means of solving inverse kinematic equations where there is a changing kinematic model. Such an approach is beneficial in applications such as the maintenance of power transmission lines, welding, and providing support to handicapped people. The method is demonstrated using a case study utilizing a sixdegree-of-freedom Kawasaki FS02 industrial robotic arm. The results from the case study demonstrate a solution for 95 per cent of all attainable Cartesian coordinates.
Magnetic resonance spectroscopy (MRS) is a non-invasive method, which can provide diagnostic information on children with brain tumours. The technique has not been widely used in clinical practice, partly because of the difficulty of developing robust classifiers from small patient numbers and the challenge of providing decision support systems (DSSs) acceptable to clinicians. This paper describes a participatory design approach in the development of an interactive clinical user interface, as part of a distributed DSS for the diagnosis and prognosis of brain tumours. In particular, we consider the clinical need and context of developing interactive elements for an interface that facilitates the classification of childhood brain tumours, for diagnostic purposes, as part of the HealthAgents European Union project. Previous MRS-based DSS tools have required little input from the clinician user and a raw spectrum is essentially processed to provide a diagnosis sometimes with an estimate of error. In childhood brain tumour diagnosis where there are small numbers of cases and a large number of potential diagnoses, this approach becomes intractable. The involvement of clinicians directly in the designing of the DSS for brain tumour diagnosis from MRS led to an alternative approach with the creation of a flexible DSS that, allows the clinician to input prior information to create the most relevant differential diagnosis for the DSS. This approach mirrors that which is currently taken by clinicians and removes many sources of potential error. The validity of this strategy was confirmed for a small cohort of children with cerebellar tumours by combining two diagnostic types, pilocytic astrocytomas (11 cases) and ependymomas (four cases) into a class of glial tumours which then had similar numbers to the other diagnostic type, medulloblastomas (18 cases). Principal component analysis followed by linear discriminant analysis on magnetic resonance spectral data gave a classification accuracy of 91% for a three-class classifier and 94% for a two-class classifier using a leave-one-out analysis. This DSS provides a flexible method for the clinician to use MRS for brain tumour diagnosis in children.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.