Book chapter
Identifying Symptom Clusters Through Association Rule Mining
Artificial Intelligence in Medicine, pp.491-496
Lecture Notes in Computer Science, 12721, Springer International Publishing
06/08/2021
DOI: 10.1007/978-3-030-77211-6_58
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.
Details
- Title: Subtitle
- Identifying Symptom Clusters Through Association Rule Mining
- Creators
- Mikayla Biggs - University of IowaCarla Floricel - University of Illinois ChicagoLisanne Van Dijk - University of Texas MD Anderson Cancer CenterAbdallah S. R Mohamed - University of Texas MD Anderson Cancer CenterC. David FullerG. Elisabeta Marai - University of Illinois ChicagoXinhua Zhang - University of Illinois ChicagoGuadalupe Canahuate - University of Iowa
- Resource Type
- Book chapter
- Publication Details
- Artificial Intelligence in Medicine, pp.491-496
- Series
- Lecture Notes in Computer Science; 12721
- DOI
- 10.1007/978-3-030-77211-6_58
- eISSN
- 1611-3349
- ISSN
- 0302-9743
- Publisher
- Springer International Publishing; Cham
- Language
- English
- Date published
- 06/08/2021
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984197182902771
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