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T1  - Machine-learned identification of psychological subgroups with relation to pain interference in patients after breast cancer treatments 
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UR  - http://hdl.handle.net/10138/313986 
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A1  - Sipilä, Reetta; Kalso, Eija; Lötsch, Jörn 
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Y1  - 2020 
LA  - eng 
AB  - 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. 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 sle... 
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KW  - 3126 Surgery, anesthesiology, intensive care, radiology; 3122 Cancers; Pain; Quality of life; Breast cancer survivers; Data science; Machine-learning; PERSONALITY; RESILIENCE; RISK; SCIENCE; SCALE; FEAR-AVOIDANCE MODEL; ASSOCIATION; SEVERITY 
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