Authors:
N. Al-Zu’bi
;
A. Al-kharabsheh
;
L. Momani
and
W. Al-Nuaimy
Affiliation:
University of Liverpool, United Kingdom
Keyword(s):
Hydrocephalus, Electronic shunt, Multi-agent.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Data Engineering
;
Design and Development Methodologies for Healthcare IT
;
Development of Assistive Technology
;
Distributed and Mobile Software Systems
;
Enterprise Information Systems
;
Expert Systems
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Healthcare Management Systems
;
Information Systems Analysis and Specification
;
Knowledge Engineering and Ontology Development
;
Knowledge Management
;
Knowledge-Based Systems
;
Mobile Technologies
;
Mobile Technologies for Healthcare Applications
;
Neural Rehabilitation
;
Neurotechnology, Electronics and Informatics
;
Ontologies and the Semantic Web
;
Society, e-Business and e-Government
;
Software Engineering
;
Symbolic Systems
;
Web Information Systems and Technologies
Abstract:
Hydrocephalus is a common chronic condition that results in excessive accumulation of cerebrospinal fluid (CSF) inside the skull, often leading to brain damage. The treatment and management of hydrocephalus remain a challenging issue, especially for diagnosis, improving current shunt treatment, and predicting shunt success. Current diagnosis procedure depends mainly on the surgeons’ observation of the clinical symptoms, neuroimages and instantaneous of intracranial pressure recording. These lack accuracy in diagnosis and predicting the outcome. Dominant treatment relies on passive mechanical shunts; these also exhibit virous problems. Adding to that, the lack of communication between the community surgeons and a limited understanding of the hydrodynamics of this disease have limited the effectiveness of hydrocephalus treatment. This paper proposes a new approach to improve the treatment and management of hydrocephalus through a multiagent cognitive system over a distributed network o
f hydrocephalus patients with intelligent shunting system. This approach will not only develop autonomous treatment method for hydrocephalus, but also it defines a method for information acquiring and analysis to better understanding hydrocephalus and assess shunt functionality.
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