ArticleArtificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- )2021
Identifying Symptom Clusters Through Association Rule Mining.
Article in Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Improving Survival Prediction of Head and Neck Cancer Patients by Modeling Long-term Symptom Burden.Proceedings. IEEE International Conference on Healthcare Informatics · 2026Article
- L-VISP: LSTM Visualization for Interpretable Symptom Prediction in Patient Cohorts.Computer graphics forum : journal of the European Association for Computer Graphics · 2026Article
- Exploring psychological symptom associations among people living with HIV using an apriori algorithm.BMC infectious diseases · 2026Article
- PRO-Based Stratification Improves Model Prediction for Toxicity and Survival of Head and Neck Cancer Patients.IEEE journal of biomedical and health informatics · 2025Article
- Roses Have Thorns: Understanding the Downside of Oncological Care Delivery Through Visual Analytics and Sequential Rule Mining.IEEE transactions on visualization and computer graphics · 2024Article
- Analysis of COVID-19 vaccine adverse event using language model and unsupervised machine learning.PloS one · 2023Article
- THALIS: Human-Machine Analysis of Longitudinal Symptoms in Cancer Therapy.IEEE transactions on visualization and computer graphics · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Cancer patients experience many symptoms throughout their cancer treatment and sometimes suffer from lasting effects post-treatment. Patient-Reported Outcome (PRO) surveys provide a means for monitoring the patient's symptoms during and after treatment. Symptom cluster (SC) research seeks to understand these symptoms and their relationships to define new treatment and disease management methods to improve patient's quality of life. This paper introduces association rule mining (ARM) as a novel alternative for identifying symptom clusters. We compare the results to prior research and find that while some of the SCs are similar, ARM uncovers more nuanced relationships between symptoms such as anchor symptoms that serve as connections between interference and cancer-specific symptoms.
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Registered trials
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