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Identifying Symptom Clusters Through Association Rule Mining
Authors: Biggs,M., Floricel, C., Van Dijk, L., Mohamed, A.S.R., Fuller, D., Marai G.E., Zhang, X., Canahuate, G.
Publication: International Conference on Artificial Intelligence in Medicine (AIME 2021), vol 12721, Springer, Cham, pp. 491-496 URL: https://doi.org/10.1007/978-3-030-77211-6_58 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. Keywords: Association rule mining; Symptom clusters; PRO Date: June 8, 2021 Document: View PDF |
