DOES ARTIFICIAL INTELLIGENCE IDENTIFY UNDERLINED MECHANISMS OF QUALITY OF LIFE IN NEGATIVE AFFECTIVITY?

ADRIÁN YORIS; TERNANDO TORRENTE; LUCAS SEDEÑO; AGUSTIN IBAÑEZ

Dresden

Congreso; First official European Congress on Clinical Psychology and Psychological Treatment of EACLIPT 2019, 31st October / 2nd November 2019, Dresden (Germany).; 2019

European Congress on Clinical Psychology and Psychological Treatment

A new issue among current mental health challenges is the high comorbidity between anxiety disorders and depression -/considered together as negative affectivity. This complexity is unattainable for most psychological treatments, designed for mutually excluded diagnostic categories. High comorbidity negatively impacts the patients? Quality of Life (Q-L) as well as its severity. Q-L is defined as patients? satisfaction with general and specific areas of own life, comprising work, health, interpersonal relationships, leisure activities, among others. Particularly, anxiety disorders and depression (Pyne et al, 1997) are associated with a significant deterioration in Q-L and functioning, compared to control groups. Regarding depression, some studies reported greater Q-L deterioration than healthy controls. Consequently, it is feasible that Q-L in depression and comorbid anxiety disorders are further impaired than anxiety disorders without comorbidity. However, few is known about how Q-L interacts with symptoms, core cognitions, and specific diagnosis inside this group of patients. In order to reveal that it is important to know what is more relevant in Q-L deterioration: if the comorbidity or severity; ii) if any symptom has more power to?.; or iii)By combining machine learning approaches, we developed a nontraditional statistical analysis to understand how Q-L interacts with different-level dimensions of anxiety-depression psychopathology. 419 anxiety-depressive outpatients completed 15 clinical questionnaires about symptoms and core cognition. Participants were grouped into 3 subgroups: high, low, and impaired Q-L, based on their scores in the Q-LES-Q-SF questionnaire and previous classification reports. We obtained three pared groups in Throughout different machine learning analyzes, we predicted the associations between the three levels of impairment of the Q-L with the symptoms and cognitions reported by the patients, concluding in the existence of a pattern of poor Q-L and high comorbidity in all participants. These interactions could be learned by machine learning to predict treatment outcomes or to guide new transdiagnostic treatments.