ArticleBreast (Edinburgh, Scotland)2020
Machine-learned identification of psychological subgroups with relation to pain interference in patients after breast cancer treatments.
Article in Breast (Edinburgh, Scotland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Artificial Intelligence and Machine Learning in Cancer Pain: A Systematic Review.Journal of pain and symptom management · 2024Pooled it
- The role of the oxytocin system in the resilience of patients with breast cancer.Frontiers in oncology · 2023Review
- Artificial intelligence empowered digital health technologies in cancer survivorship care: A scoping review.Asia-Pacific journal of oncology nursing · 2022Article
- Resilience, pain, and health-related quality of life in gynecological patients undergoing surgery for benign and malignant conditions: a 12-month follow-up study.BMC women's health · 2022Observational
- Phenomapping of Patients with Primary Breast Cancer Using Machine Learning-Based Unsupervised Cluster Analysis.Journal of personalized medicine · 2021Article
- Review
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPersistent 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.
methodsWe 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.
resultsArtificial 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.
conclusionsThe 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.
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