An integrative ambient agent model for unipolar depression relapse prevention

Abstract

One of the challenges for persons with a history of unipolar depression is to stay healthy throughout their lifetime. In principle, having more severe prior onset cases escalates the risk to fall into a relapse. In this article, first a domain model of the process of depression, recovery and relapse is presented, and second an integrative ambient agent model to support persons from relapse is described. Based on several personal characteristics and a representation of events (i.e., life events or daily hassles) the domain model can simulate whether a human that recovered from a depression will fall into a relapse or recurrence. A number of well-known relations between events and the course of depression are summarized from the literature and it is shown that the domain model exhibits those patterns. The domain model has been mathematically analyzed to find out which stable situations exist. Second, by incorporating this domain model into an ambient agent system, the resulting integrative ambient agent model is able to reason about the state of the human and the effect of possible actions. Several simulation experiments have been conducted to illustrate the functioning of the proposed model in different scenarios. In addition, an automated verification method using Temporal Trace Language (TTL) is used to verify that the ambient agent model satisfies a number of relevant properties. Finally, it is pointed out how this model can be used in depression therapy, supported by an ambient agent.

An adaptive Network-Oriented cognitive model for Major Depression and its treatment

“…The cognitive model presented here can be used to develop humanaware intelligent systems (Azziz, Klein, & Treur, 2010;Bosse, Callaghan, & Lukowicz, 2010) that can provide help getting more understanding of the deficits and impaired pathways, supporting persons suffering from Major Depression Disorder. Treatments, especially Deep Brain Stimulation, have proven to be effective (El Mansari et al, 2010;Schlaepfer et al, 2008;Schlaepfer et al, 2014) and a computational model can be of benefit to the research for treatments and biology of MDD.…”

Internet of Things for enabling smart environments: A technology-centric perspective

“…This detection concerns one of the most common heart problems in the world and is a subject of research nowadays by means of using phones and watches. Situations such as relapse in depression can also be detected and analyzed [5]. Such system can permit to act as soon as possible to prevent the state of the person to escalate.…”

Artificial intelligence and ambient intelligence

“…Health is another field of research where agents and Semantic Web technologies have been applied. As an example, Aziz et al [8] have developed an agent-based system to prevent unipolar depression relapse. Several ontologies have been developed to model the depression process, including the recovery and relapse stages.…”