|Title:||Technological infrastructure and business intelligence strategies for the EDEVITALZH eHealth Delivery System||Authors:||Pérez Del Pino, M. A.
García Báez, P.
Martinez-Garcia, J. M.
Suarez-Araujo, C. P.
|UNESCO Clasification:||120304 Inteligencia artificial
32 Ciencias médicas
|Issue Date:||2014||Journal:||Topics in intelligent engineering and informatics||Conference:||1st Australian Conference on the Applications of Systems Engineering (ACASE)||Abstract:||In the recent years, the sociological importance of the elderly has grown significantly because of the increase of the prevalence of degenerative disorders, among which Mild Cognitive Impairment (MCI), Alzheimer's Disease (AD) and other cortical dementias should be highlighted. Using actual diagnostic criteria, by the time a patient is diagnosed with AD, the pathology has spread itself during several years. Besides, the unique certain AD diagnosis can only be performed postmortem. Thus, it becomes absolutely necessary to accomplish early detection and MCI diagnosis. This chapter presents the advances achieved in the fields of Medical Informatics and Telemedicine by means of the EDEVITALZH eHealth Delivery System. On one hand, and of remarkable importance to succeed in a certain diagnosis, a standardization of the clinical protocol is proposed and digitally implemented to provide clinicians with the adequate procedures, methods and tools to perform more accurate early and differential diagnosis, and prognosis of patients affected by these neuropathologies. On the other hand, EDEVITALZH technological infrastructure is described, regarding computer architecture, databases and software engineering, with special focus on the embedded mechanisms that allow integration between EDEVITALZH Core components and the Intelligent Systems for Diagnosis (ISD) and the Intelligent Decision Support Tools (IDST), providing Computational Intelligence to the described virtual clinical environment. These integrations upgrade EDEVITALZH to an intelligent Clinical Workstation, being able to aid clinicians in decision making for early and differential diagnosis, prognosis and in performing evolution studies of patients and their pathologies, no matter which healthcare level patients are being assisted in, Primary or Specialized.||URI:||http://hdl.handle.net/10553/72233||ISBN:||978-3-319-01435-7||ISSN:||2193-9411||DOI:||10.1007/978-3-319-01436-4_8||Source:||Advanced Methods And Applications In Computational Intelligence / Ryszard Klempous [et al.] (eds.). TIEI, v. 6, p. 145-164, (2014)|
|Appears in Collections:||Actas de congresos|
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