Abstract
Modern neuroscience imaging technologies considerably affect diagnostic and prognostic accuracy and facilitate progress towards the cure of brain diseases. The benefits largely depend on the practicalities by which the large-scale imaging and clinical data can be integrated, examined and understood. In EU neuGRID4You (N4U) project, many datasets were generated from research centres and hospitals. In order to perform effective analyses, these datasets and their metadata along with a number of pre-computed parameters are stored in a big data repository. This paper focuses on the patient identification using big data and Fuzzy Logic, which has been achieved through fuzzy processing where a reference number called Alzheimers Disease Identification Number (ADIN) is calculated. It has enabled patients sorting for a particular intensity of Alzheimers disease, short-term estimation of the progression of that disease and context of individual patients with respect to other patients such as appropriate treatment, estimated life expectancy etc. The generated rules define the necessary knowledge base for the inference engine to generate output sets and an aggregate membership function of each rule is formed. Using this function, a most representative value of the total output set is obtained which represents the disease intensity. The implemented system and its evaluation are based on realistic datasets, demonstrators and making use of real-life neuroscience case studies. The presented results of four selected case studies show that this approach have provided sufficient expressiveness in understanding patients’ disease information. Finally, a discussion and conclusions are presented on the opportunities offered by the calculation of ADIN to manage Alzheimers disease along with potential future extensions or applications of this work.
Highlights
Exceptional increase in the generation and availability of clinical and neuroimaging data sets has forced the advancements in data processing infrastructures and analysis applications
Massive amount of data is being continuously and anonymously shared by the hospitals and research centers to constitute the foundations of brain disease analyses, such as Alzheimer
When dealing with a patient who has affected more than three areas of cognition, the Alzheimer’s Disease Identification Number (ADIN) is calculated by using the memory loss and the other two symptoms with a higher score
Summary
Exceptional increase in the generation and availability of clinical and neuroimaging data sets has forced the advancements in data processing infrastructures and analysis applications. In the neuGRID4You project alone, thousands of clinical parameters were dealt with To achieve these necessary pre-computations, fuzzy processing can be applied; for example, to compute the intensity of a disease and to provide quick sorting of all patients based on a disease intensity number. (a) the practicalities of transforming and storing neuroscience population’s massive amounts of heterogeneous datasets including data dictionaries and images; (b) formulation and enabling run-time disease intensity numbers calculation using fuzzy processing, their formats, storage structure and retrieval; (c) supporting end users in formulating appropriate studies for analysis using clinical datasets, images and pre-computed patients’ classifications i.e. based on disease intensity number(s).
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