Biggs Mikayla, Floricel Carla, Van Dijk Lisanne, Mohamed Abdallah S R, Fuller C David, Marai G Elisabeta, Zhang Xinhua, Canahuate Guadalupe
University of Iowa, Iowa City, IA, USA.
University of Illinois in Chicago, Chicago, IL, USA.
Artif Intell Med Conf Artif Intell Med (2005-). 2021 Jun;12721:491-496. doi: 10.1007/978-3-030-77211-6_58. Epub 2021 Jun 8.
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.
癌症患者在整个癌症治疗过程中会经历多种症状，有时还会遭受治疗后的长期影响。患者报告结局（PRO）调查提供了一种在治疗期间和治疗后监测患者症状的方法。症状群（SC）研究旨在了解这些症状及其关系，以定义新的治疗和疾病管理方法，从而提高患者的生活质量。本文介绍了关联规则挖掘（ARM）作为一种识别症状群的新方法。我们将结果与先前的研究进行比较，发现虽然有些症状群相似，但ARM揭示了症状之间更细微的关系，例如作为干扰症状和癌症特异性症状之间联系的锚定症状。
