Identifying Symptom Clusters Through Association Rule Mining.

Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ) Pub Date : 2021-06-01 Epub Date: 2021-06-08 DOI:10.1007/978-3-030-77211-6_58

Mikayla Biggs, Carla Floricel, Lisanne Van Dijk, Abdallah S R Mohamed, C David Fuller, G Elisabeta Marai, Xinhua Zhang, Guadalupe Canahuate

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引用次数: 1

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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通过关联规则挖掘识别症状聚类。

癌症患者在整个癌症治疗过程中会经历许多症状，有时会在治疗后遭受持久的影响。患者报告的结果(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，以确定新的治疗和疾病管理方法，以改善患者的生活质量。本文介绍了关联规则挖掘(ARM)作为识别症状聚类的一种新方法。我们将结果与先前的研究进行了比较，发现虽然一些SCs是相似的，但ARM揭示了症状之间更微妙的关系，例如锚定症状，它作为干扰和癌症特异性症状之间的联系。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- )

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