Machine Learning in Health Psychology

Project leader: Björn Eskofier
Project members: Arne Küderle, Stefan Gradl, Björn Eskofier, Nicolas Rohleder
Start date: 1. September 2018
End date:

Abstract

Stress is a hidden epidemic – the World Health Organization estimates that mental diseases, including stress-related disorders, will be the second leading cause of disabilities by the year 2020. Since the negative economic impact of stress is substantial, there is an interest in detecting stress-related diseases as early as possible for early intervention, such as precision medicine approaches.

Stress can be differentiated into chronic and acute stress. As an example, social interactions with others can trigger acute stress. This response is characterized by strong biological reactions that affect the whole body through widely spread autonomic innervation and the secretion of stress hormones. Whereas adequate stress responses are a crucial and healthy physiological reaction, defective stress responses have been linked to DNA damage, over-expression of inflammatory genes, and declines in cognitive functioning, which are well known markers of physiological and biological age.

Therefore, the goal of this work is to use machine learning methods for the classification of those stress response patterns. Compared to classification by a trained professional this approach has the potential to reduce the required time, as well as increase the objectivity of the grouping.