MADRIM: A major depression remote intelligent monitor

Mugica, Francisco; Nebot, Àngela; Bagherpour, Solmaz; Serrano‐Blanco, Antoni; Baladón, Luisa

doi:10.1109/fuzz-ieee.2012.6251148

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…The patient assessment unit is already developed and it is based on rule-based models derived from experience and expert knowledge (Mugica et al, 2012). The prediction unit is still being developed and it is based on knowledge extracted from real data by means of the Fuzzy Inductive Reasoning (FIR) data mining technique (Escobet et al, 2008).…”

Section: Intelligent Remote Monitoring Systemmentioning

confidence: 99%

Mobile App and Website for Major Depression Monitoring

Nebot¹,

Mugica²,

Abdollahi³

2013

Proceedings of the 3rd International Conference on Simulation and Modeling Methodologies, Technologies and Applications

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One of the challenges for the patients diagnosed with major depression is not to experience relapse or reoccurrence which are very common characteristics of major depression. Providing constant monitoring of these patients during their daily life for the first year of their depression can have a significant impact on preventing these patients to experience reoccurrence and relapse. In this paper we describe an intelligent remote monitoring system that is in the process of development and present the new research done centered on the interaction between the system and the actors involved, i.e. patients, psychiatrists and primary care physicians. This interaction is done through an android application for mobile telephones and a Website. The specification and design of the information requested and submitted to system actors through both platforms is performed by the communication module, which is also described in this research.

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Section: Intelligent Remote Monitoring Systemmentioning

confidence: 99%

Mobile App and Website for Major Depression Monitoring

Nebot¹,

Mugica²,

Abdollahi³

2013

Proceedings of the 3rd International Conference on Simulation and Modeling Methodologies, Technologies and Applications

View full text Add to dashboard Cite

show abstract

“…Classification SVM model is shown using a genetic algorithm to select the feature classification accuracy of 88.6% in patients with depression achieve. in this article [10], Major depression is an intelligent remote monitor, called MADRIM is provided, the main goal is to follow the evolution of MADRIM patients during their recovery in order to understand their behavior and to prevent relapse or recurrence is. due to the considerable lack of research done on the effectiveness of using neural networks for the diagnosis of major depression And also consider the need to take advantage of neural networks in the diagnosis of mood disorders and reciprocate by expanding public-their anonymity, this paper is dedicated to providing a method for the diagnosis of major depression using neural networks, will.…”

Section: History Researchmentioning

confidence: 99%