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Erschienen in:
The Power of the “Pursuit” Learning Paradigm in the Partitioning of Data Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

An Adaptive Temporal-Causal Network Model for Stress Extinction Using Fluoxetine

verfasst von : S. Sahand Mohammadi Ziabari

Erschienen in: Artificial Intelligence Applications and Innovations

Verlag: Springer International Publishing

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Abstract

In this paper, an adaptive temporal causal network model based on drug therapy named fluoxetine to decrease the stress level of post-traumatic stress disorder is presented. The stress extinction is activated by a cognitive drug therapy (here fluoxetine) that uses continuous usage of medicine. The aim of this therapy is to reduce the connectivity between some components inside the brain which are responsible for causing stress. This computational model aspires to realistically demonstrate the activation of different portions of brain when the therapy is applied. The cognitive model starts with a situation of strong and continuous stress in an individual and after using fluoxetine the stress level begins to decrease over time. As a result, the patient will have a reduced stress level compared to not using drug.

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Vorheriges Kapitel Mixture Modules Based Intelligent Control System for Autonomous Driving
Nächstes Kapitel Clustering Diagnostic Profiles of Patients
Fußnoten
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Metadaten
Titel
An Adaptive Temporal-Causal Network Model for Stress Extinction Using Fluoxetine
verfasst von
S. Sahand Mohammadi Ziabari
Copyright-Jahr
2019
Buch
Artificial Intelligence Applications and Innovations

Print ISBN: 978-3-030-19822-0

Electronic ISBN: 978-3-030-19823-7

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-19823-7_8