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Identifying Symptom Clusters Through Association Rule Mining.

Mikayla Biggs1, Carla Floricel2, Lisanne Van Dijk3

  • 1University of Iowa, Iowa City, IA, USA.

Artificial Intelligence in Medicine. Conference on Artificial Intelligence in Medicine (2005- )
|September 20, 2021
PubMed
Summary
This summary is machine-generated.

Association rule mining (ARM) offers a new way to find symptom clusters in cancer patients. This method reveals detailed symptom connections, improving quality of life through better symptom management.

Keywords:
Association rule miningPROSymptom clusters

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Area of Science:

  • Oncology
  • Data Science
  • Health Informatics

Background:

  • Cancer treatment causes numerous symptoms, impacting patient quality of life.
  • Patient-Reported Outcome (PRO) surveys are crucial for monitoring symptoms during and after treatment.
  • Symptom cluster (SC) research aims to understand symptom relationships for improved management.

Purpose of the Study:

  • To introduce Association Rule Mining (ARM) as a novel method for identifying symptom clusters in cancer patients.
  • To compare ARM-derived symptom clusters with those found through traditional research.
  • To explore the potential of ARM in uncovering nuanced symptom relationships.

Main Methods:

  • Applied Association Rule Mining (ARM) to analyze patient-reported outcome data.
  • Identified symptom clusters and their relationships within the cancer patient population.
  • Compared the identified clusters with previously established symptom clusters from prior research.

Main Results:

  • ARM successfully identified symptom clusters, with some overlap and some novel findings compared to prior research.
  • ARM revealed nuanced relationships between symptoms, including 'anchor symptoms'.
  • Anchor symptoms were identified as key connectors between symptom interference and cancer-specific symptoms.

Conclusions:

  • Association Rule Mining (ARM) is a promising alternative for identifying complex symptom clusters in cancer care.
  • ARM enhances understanding of symptom relationships, potentially leading to more targeted interventions.
  • This approach can improve patient quality of life by refining symptom management strategies.