This project investigates how machine learning can be used to identify and characterize stress response patterns in health psychology. It is motivated by the need to detect stress-related dysregulation early and more objectively, especially when traditional classification depends on time-intensive expert assessment.
The work focuses on acute and chronic stress responses, including their physiological and behavioral manifestations, and studies how computational models can support more scalable, precise, and clinically useful grouping of stress phenotypes.
Related Publications
2019
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Luca Abel, Robert Richer, Arne Küderle, and 3 more authors
In Proc of the 13th EAI Int Conf Pervasive Comput Technol for Healthc - PervasiveHealth’19, 2019
Modern machine learning techniques enable new possibilities for the analysis of psychological data. In the field of health psychology, it is of interest to explore the biological processes triggered by acute stress. This work introduces a method to automatically classify individuals into distinct stress responder groups based on these biological processes. Two important stress-sensitive markers were used: Salivary cortisol and Interleukin-6 (IL-6) in blood plasma. Controlled stress was induced using the Trier Social Stress Test on two consecutive days. Results show that Support Vector Machines performed best on the given dataset. We distinguished four different cortisol and three different IL-6 responder types with high mean accuracies (92.2% \textpm 9.7% and 91.2% \textpm 6.3%, respectively). Classification results were mainly limited by class imbalances and high intra-class standard deviations. Whereas promising as a first application of machine learning on such datasets, generalizability and real-world applicability of our results need to be proven by further research.
