Open AccessConcordance networks and application to clustering cancer symptomology
Author(s) -
Teague Henry,
Sarah A. Marshall,
Nancy E. Avis,
Beverly Levine,
Edward H. Ip
Publication year - 2018
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0191981
Subject(s) - concordance , breast cancer , cluster analysis , bridge (graph theory) , medicine , cancer , depression (economics) , cognition , bioinformatics , computer science , psychiatry , artificial intelligence , biology , economics , macroeconomics
Symptoms of complex illnesses such as cancer often present with a high degree of heterogeneity between patients. At the same time, there are often core symptoms that act as common drivers for other symptoms, such as fatigue leading to depression and cognitive dysfunction. These symptoms are termed bridge symptoms and when combined with heterogeneity in symptom presentation, are difficult to detect using traditional unsupervised clustering techniques. This article develops a method for identifying patient communities based on bridge symptoms termed concordance network clustering. An empirical study of breast cancer symptomatology is presented, and demonstrates the applicability of this method for identifying bridge symptoms.