Seeking automatic recognition of depressive symptoms

The Madrid Médico magazine has echoed cutting-edge research carried out at LabLeni led by researcher Javier Marín. Computational Psychiatry aims to develop powerful artificial intelligence (AI) models to recognize biomarker patterns in mental health disorders, and in this context, we conducted a study with 100 patients with these interesting results: the control group showed affiliative body movements through lateral head turns, while subjects with depressive symptoms made forward and backward movements, possibly related to increased stress, as well as avoidance of eye contact. In terms of speech, participants with depressive symptoms used more words associated with denial and exclusion and referred more to negative emotions. Controls more frequently used words related to friendship and health. At a neurophysiological level, subjects with depressive symptoms showed lower sympathetic activity, reflected in the relationship between heart rate variability and electrodermal activity. These results are in line with the Emotional Context Insensitivity hypothesis and were demonstrated for the first time in conversations with HV. In conclusion, we are facing reliable, objectifiable, standardizable, and comparable bioinformatics tools that can be of great help for the early detection of affective disorders.
Read more in: https://secure.webpublication.es/210352/1987548/#page=26