Machine-learned identification of psychological subgroups with relation to pain interference in patients after breast cancer treatments

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http://hdl.handle.net/10138/313986

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Sipilä , R , Kalso , E & Lötsch , J 2020 , ' Machine-learned identification of psychological subgroups with relation to pain interference in patients after breast cancer treatments ' , Breast , vol. 50 , pp. 71-80 . https://doi.org/10.1016/j.breast.2020.01.042

Title: Machine-learned identification of psychological subgroups with relation to pain interference in patients after breast cancer treatments
Author: Sipilä, Reetta; Kalso, Eija; Lötsch, Jörn
Contributor: University of Helsinki, HUS Perioperative, Intensive Care and Pain Medicine
University of Helsinki, HUS Perioperative, Intensive Care and Pain Medicine
Date: 2020-04
Language: eng
Number of pages: 10
Belongs to series: Breast
ISSN: 0960-9776
URI: http://hdl.handle.net/10138/313986
Abstract: 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 sleep-related parameters as well as parameters related to pain intensity and interference had been acquired. Data were analysed by using supervised and unsupervised 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 interference and impact on life. Results: Artificial intelligence-based detection of data structure, implemented as self-organizing neuronal maps, identified two different clusters of patients. A smaller cluster (11.5% of the patients) 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 comparatively more impeded by persistent pain. Conclusions: The results support the initial hypothesis that psychological and sleep-related parameter 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. (C) 2020 The Authors. Published by Elsevier Ltd.
Subject: 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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