Identifying Symptom Clusters Through Association Rule Mining

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

doi:10.1007/978-3-030-77211-6_58

Identifying Symptom Clusters Through Association Rule Mining

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

Radiation Oncology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

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.

Original language	English (US)
Title of host publication	Artificial Intelligence in Medicine - 19th International Conference on Artificial Intelligence in Medicine, AIME 2021, Proceedings
Editors	Allan Tucker, Pedro Henriques Abreu, Jaime Cardoso, Pedro Pereira Rodrigues, David Riaño
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	491-496
Number of pages	6
ISBN (Print)	9783030772109
DOIs	https://doi.org/10.1007/978-3-030-77211-6_58
State	Published - 2021
Event	19th International Conference on Artiﬁcial Intelligence in Medicine, AIME 2021 - Virtual, Online Duration: Jun 15 2021 → Jun 18 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12721 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th International Conference on Artiﬁcial Intelligence in Medicine, AIME 2021
City	Virtual, Online
Period	6/15/21 → 6/18/21

Keywords

Association rule mining
PRO
Symptom clusters

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-77211-6_58

Cite this

Biggs, M., Floricel, C., Van Dijk, L., Mohamed, A. S. R., David Fuller, C., Marai, G. E., Zhang, X., & Canahuate, G. (2021). Identifying Symptom Clusters Through Association Rule Mining. In A. Tucker, P. Henriques Abreu, J. Cardoso, P. Pereira Rodrigues, & D. Riaño (Eds.), Artificial Intelligence in Medicine - 19th International Conference on Artificial Intelligence in Medicine, AIME 2021, Proceedings (pp. 491-496). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12721 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-77211-6_58

Identifying Symptom Clusters Through Association Rule Mining. / Biggs, Mikayla; Floricel, Carla; Van Dijk, Lisanne et al.
Artificial Intelligence in Medicine - 19th International Conference on Artificial Intelligence in Medicine, AIME 2021, Proceedings. ed. / Allan Tucker; Pedro Henriques Abreu; Jaime Cardoso; Pedro Pereira Rodrigues; David Riaño. Springer Science and Business Media Deutschland GmbH, 2021. p. 491-496 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12721 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Biggs, M, Floricel, C, Van Dijk, L, Mohamed, ASR , David Fuller, C, Marai, GE, Zhang, X & Canahuate, G 2021, Identifying Symptom Clusters Through Association Rule Mining. in A Tucker, P Henriques Abreu, J Cardoso, P Pereira Rodrigues & D Riaño (eds), Artificial Intelligence in Medicine - 19th International Conference on Artificial Intelligence in Medicine, AIME 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12721 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 491-496, 19th International Conference on Artiﬁcial Intelligence in Medicine, AIME 2021, Virtual, Online, 6/15/21. https://doi.org/10.1007/978-3-030-77211-6_58

Biggs M, Floricel C, Van Dijk L, Mohamed ASR , David Fuller C, Marai GE et al. Identifying Symptom Clusters Through Association Rule Mining. In Tucker A, Henriques Abreu P, Cardoso J, Pereira Rodrigues P, Riaño D, editors, Artificial Intelligence in Medicine - 19th International Conference on Artificial Intelligence in Medicine, AIME 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 491-496. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-77211-6_58

Biggs, Mikayla ; Floricel, Carla ; Van Dijk, Lisanne et al. / Identifying Symptom Clusters Through Association Rule Mining. Artificial Intelligence in Medicine - 19th International Conference on Artificial Intelligence in Medicine, AIME 2021, Proceedings. editor / Allan Tucker ; Pedro Henriques Abreu ; Jaime Cardoso ; Pedro Pereira Rodrigues ; David Riaño. Springer Science and Business Media Deutschland GmbH, 2021. pp. 491-496 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Identifying Symptom Clusters Through Association Rule Mining

Abstract

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Conference

Keywords

ASJC Scopus subject areas

Access to Document

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