Ontology-Mediated Distributed Decision Support for Breast Cancer.
ABSTRACT We have developed a prototype system to support decision making in Breast Cancer, wherein the varied nature of expertise is
modelled by multiple ontologies that provide domain-specific grounding to concepts and relationships used. While the different
medical experts need to be co-present at a meeting, our system employs a distributed architecture for handling data and invoking
services appropriate for the requirements of this decision-making process. This distributed system is built upon Semantic
Web technology, which enables the possibility of Web-based tele-medicine.
Conference Paper: Linking Image Structures with Medical Ontology Information.[Show abstract] [Hide abstract]
ABSTRACT: Medical ontologies are being developed with some of these specifically for mammographic computer aided diagnosis (CAD) systems. However, to provide full functionality for such mammographic CAD systems it is essential that the ontology information is fully linked to the image information. This linking can be through problem specific image attributes. However, such an approach tends to be non-generic. Here, we propose a framework that will use generic image structures and the topology that links the image structures. In the process we describe a comparison approach which takes the classes, attributes and semantics into account.Digital Mammography, 8th International Workshop, IWDM 2006, Manchester, UK, June 18-21, 2006, Proceedings; 01/2006
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ABSTRACT: In recent years, mild cognitive impairment (MCI) has attracted significant attention as an indicator of high risk for Alzheimer's disease (AD), and the diagnosis of MCI can alert patient to carry out appropriate strategies to prevent AD. To avoid subjectivity in diagnosis, we propose an ontology driven decision support method which is an automated procedure for diagnosing MCI through magnetic resonance imaging (MRI). In this approach, we encode specialized MRI knowledge into an ontology and construct a rule set using machine learning algorithms. Then we apply these two parts in conjunction with reasoning engine to automatically distinguish MCI patients from normal controls (NC). The rule set is trained by MRI data of 187 MCI patients and 177 normal controls selected from Alzheimer's Disease Neuroimaging Initiative (ADNI) using C4.5 algorithm. By using a 10-fold cross validation, we prove that the performance of C4.5 with 80.2% sensitivity is better than other algorithms, such as support vector machine (SVM), Bayesian network (BN) and back propagation (BP) neural networks, and C4.5 is suitable for the construction of reasoning rules. Meanwhile, the evaluation results suggest that our approach would be useful to assist physicians efficiently in real clinical diagnosis for the disease of MCI.Computer methods and programs in biomedicine 01/2014; · 1.56 Impact Factor
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ABSTRACT: We present an agent-based distributed decision support system for the diagnosis and prognosis of brain tumors developed by the HEALTHAGENTS project. HEALTHAGENTS is a European Union funded research project, which aims to enhance the classification of brain tumours using such a decision support system based on intelligent agents to securely connect a network of clinical centres. The HEALTHAGENTS system is implementing novel pattern recognition discrimination methods, in order to analyse in vivo Magnetic Resonance Spectroscopy (MRS) and ex vivo/in vitro High Resolution Magic Angle Spinning Nuclear Magnetic Resonance (HR-MAS) and DNA micro-array data. HEALTHAGENTS intends not only to apply forefront agent technology to the biomedical field, but also develop the HEALTHAGENTS network, a globally distributed information and knowledge repository for brain tumour diagnosis and prognosis.Applied Intelligence 01/2009; 30(3):191-202. · 1.85 Impact Factor