Background: Persistent pain in breast cancer survivors is common. Psychological and sleep-related factors modulate perception, interpretation and coping with pain and may contribute to the clinical phenotype of persistent pain after breast cancer treatments. The present analysis pursued the hypothesis that breast cancer survivors form subgroups, based on psychological and sleep-related parameters that are relevant to the impact of pain on the patients’ life.
Methods: We analysed 337 women treated for breast cancer, in whom psychological and sleep-related parameters as well as parameters related to pain intensity and  interference had been ac-quired. Data were analysed by using superwised and unsuperwised machine-learning techniques (i) to detect patient subgroups based on the pattern of psychological or sleep-related parameters, (ii) to interpret the detected cluster structure and (iii) to relate this data structure to pain interfer-ence and impact on life.
Results: Artificial intelligence based detection of data structure, implemented as self-organising neuronal maps, identified two different clusters of patients. A smaller cluster (11.5 % of the pa-tients) had comparatively lower resilience, more depressive symptoms and lower extraversion than the other patients. In these patients, life-satisfaction, mood, and life in general were compar-atively more impeded by persistent pain.
Conclusions: The results support the initial hypothesis that psychological and sleep-related parame-ter patterns are meaningful for subgrouping patients with respect to how persistent pain after breast cancer treatments interferes with their life. This indicates that management of pain should address more complex features than just pain intensity. Artificial intelligence is a useful tool in the identification of subgroups of patients based on psychological factors.

MACHINE-LEARNING DERIVED PATTERNS OF PSYCHOLOGICAL PARAMETERS RELATED TO PAIN INTENSITY AND INTERFERENCE IN PATIENTS TREATED FOR BREAST CANCER