Synthesis of Characteristic Descriptors for the Detection of Distress

The success of automated distress detection to a large extent depends on the proper choice of machine learning methods and the appropriate representation of data. In the present study, we evaluate five methods for the synthesis of characteristic descriptors that are appropriate in automated distress detection. These allow efficient data representations and contribute towards a significant reduction of the computational demands. Based on these methods, we synthesized nine alternative characteristic descriptors, which were evaluated in a common experimental protocol. The experimental setup relied on a dataset collected from approximately 6000 oncological patients at different stages of therapy. The dataset consists of the binary responses to specific questions in a purposely-designed questionnaire for self-evaluation of the degree of distress. The experimental results show that the characteristic descriptor referred to as KR8 outperforms the others in terms of detection accuracy without a significant increase in the time required for modeling and classification.