An Agent Model of Temporal Dynamics in Relapse and Recurrence in Depression

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Abstract

Unipolar depression is a mental disorder characterized by a persistent low mood and loss of awareness or contentment in usual activities [1]. Despite the modern era of pharmaceutical and holistic intervention, one of the primary problems with unipolar depression (i.e. a depression not related to other mental disorders) is that it has a very high rate of recurrent and relapse cases. At least 60 percent of individuals who have had one depressive episode will have another, 70 percent of individuals who have had two depressive episodes will have a third, and 90 percent of individuals with three episodes will have a fourth episode. Although the risk of relapse may decline with time, even for those who remain well for 5 years after an index episode, the rate of recurrence/relapse is 58 percent. Despite the magnitude of the problem of recurrence and relapse, little attention has been focused on the symptom pattern in recurrent episodes of major depression [4]. In practice, there is a need to have a mechanism to monitor the condition of individuals who have had a previous encounter with unipolar depression, eventually improving their quality of life. In order to achieve this objective, the aim of the embedding research project is to develop an agent-based application that is able to support humans in the long term. The software agent is expected to have capabilities to understand its environment and the individual, providing a better monitoring and assessment of the situation. To implement this capability in any software agent, it is required to incorporate a human agent model that shows how humans might fall into relapse/recurrence or stay healthy. In case a relapse or recurrence is predicted, the agent can provide to support by providing adequate remedies. In this model, there are four major components that will represent dynamic interactions of human agent abilities involved in recurrence/relapse namely; environment, personality, social support, and coping strategies [2] [3] [4] [5] [6]. By combining these characteristics together, it will allow a hypothesis or expected behavior for the human agent to be monitored.

Original languageEnglish
Pages (from-to)279-280
Number of pages2
JournalBelgian/Netherlands Artificial Intelligence Conference
Publication statusPublished - 2009
Event21st Benelux Conference on Artificial Intelligence (BNAIC 2009) - Eindhoven, Netherlands
Duration: 29 Oct 200930 Oct 2009

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