ArticleBMC medical research methodology2021
Symptom clusters among cancer survivors: what can machine learning techniques tell us?
Article in BMC medical research methodology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Symptom clusters and their impact on quality of life in multiple myeloma survivors: Secondary analysis of BMT CTN 0702 trial.British journal of haematology · 2024Trial
- Factors associated with health-related quality of life in survivors of childhood-onset and adult-onset cancer compared to controls.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Article
- Transdiagnostic Symptoms in Electronic Health Records of Sick-Listed Employees.Journal of occupational rehabilitation · 2026Article
- Latent profile analysis for assessing symptom clusters in women with breast cancer.Journal of cancer survivorship : research and practice · 2026Article
- One size does not fit all: data-driven insights of patient-reported outcomes to tailor supportive care in breast cancer.Breast cancer research : BCR · 2026Article
- Article
- Network analysis used to investigate the interplay among somatic and psychological symptoms in patients with cancer and cancer survivors: a scoping review.Journal of cancer survivorship : research and practice · 2025Article
- Psychometric Properties of the Depression Anxiety Stress Scales (DASS-42 and DASS-21) in Patients with Hematologic Malignancies.Journal of clinical medicine · 2025Article
- Heterogeneity of pain-fatigue-sleep disturbance symptom clusters in lung cancer patients after chemotherapy: a latent profile analysis.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2024Article
- Factors influencing psychological distress among breast cancer survivors using machine learning techniques.Scientific reports · 2024Article
- Symptom cluster among cancer survivors from a nationally representative survey: a network analysis.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2024Article
- Toward an improved conceptualization of emotions in patients with cancer.Frontiers in psychiatry · 2024Review
- Changes in Sexuality and Sexual Dysfunction over Time in the First Two Years after Treatment of Head and Neck Cancer.Cancers · 2023Article
- Article
- SymptomGraph: Identifying Symptom Clusters from Narrative Clinical Notes using Graph Clustering.Proceedings of the ... Symposium on Applied Computing. Symposium on Applied Computing · 2023Article
- Artificial intelligence empowered digital health technologies in cancer survivorship care: A scoping review.Asia-Pacific journal of oncology nursing · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeKnowledge regarding symptom clusters may inform targeted interventions. The current study investigated symptom clusters among cancer survivors, using machine learning techniques on a large data set.
methodsData consisted of self-reports of cancer survivors who used a fully automated online application 'Oncokompas' that supports them in their self-management. This is done by 1) monitoring their symptoms through patient reported outcome measures (PROMs); and 2) providing a personalized overview of supportive care options tailored to their scores, aiming to reduce symptom burden and improve health-related quality of life. In the present study, data on 26 generic symptoms (physical and psychosocial) were used. Results of the PROM of each symptom are presented to the user as a no well-being risk, moderate well-being risk, or high well-being risk score. Data of 1032 cancer survivors were analysed using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) on high risk scores and moderate-to-high risk scores separately.
resultsWhen analyzing the high risk scores, seven clusters were extracted: one main cluster which contained most frequently occurring physical and psychosocial symptoms, and six subclusters with different combinations of these symptoms. When analyzing moderate-to-high risk scores, three clusters were extracted: two main clusters were identified, which separated physical symptoms (and their consequences) and psycho-social symptoms, and one subcluster with only body weight issues.
conclusionThere appears to be an inherent difference on the co-occurrence of symptoms dependent on symptom severity. Among survivors with high risk scores, the data showed a clustering of more connections between physical and psycho-social symptoms in separate subclusters. Among survivors with moderate-to-high risk scores, we observed less connections in the clustering between physical and psycho-social symptoms.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.