Health diagnostic and disease decision support systems based on personalized medical sensors

OData support
Supervisor:
Dr. Lengyel László
Department of Automation and Applied Informatics

Traditional healthcare models behave in a reactive manner and are episodic in nature. After only the emergence and aggravation of health disease searches the old model treatment for the patient. People who suffers from chronic diseases start the therapy late which means increased health costs and higher increase in chance of morbidity.

In case of the continuous health monitoring of the patient and the analysis of the incoming data could help identifying tendencies of health regression in the early phases of the diseases. The citizens should monitor themselves without direct contact of a professional supervisor who are substituted with comprehensive decision support algorithms. The aftermath is that the health institutes can focus their resources on emergency cases.

In the last decode the ICT technologies reached the maturity level of making possible the realization of the new patient-centric model. The market already provide some solutions and the new paradigm has begun to spread in the health industry.

In my thesis I give an overview about the economic benefits and the overall need of change the existing services. In addition, I also present the existing e-Health solutions on the market and their characteristics.

I also specify my proposed architectural solution for the problem and it’s inner working and design decisions, which is capable of processing large amount of data.

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