The nested structure of cancer symptoms

Implications for analyzing co-occurrence and managing symptoms

Suresh Bhavnani, G. Bellala, A. Ganesan, R. Krishna, P. Saxman, C. Scott, M. Silveira, C. Given

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Objective: Although many cancer patients experience multiple concurrent symptoms, most studies have either focused on the analysis of single symptoms, or have used methods such as factor analysis that make a priori assumptions about how the data is structured. This article addresses both limitations by first visually exploring the data to identify patterns in the co-occurrence of multiple symptoms, and then using those insights to select and develop quantitative measures to analyze and validate the results. Methods: We used networks to visualize how 665 cancer patients reported 18 symptoms, and then quantitatively analyzed the observed patterns using degree of symptom overlap between patients, degree of symptom clustering using network modularity, clustering of symptoms based on agglomerative hierarchical clustering, and degree of nestedness of the symptoms based on the most frequently co-occurring symptoms for differentsizes of symptom sets. These results were validated by assessing the statistical significance of the quantitative measures through comparison with random networks of the same size and distribution. Results: The cancer symptoms tended to cooccur in a nested structure, where there was a small set of symptoms that co-occurred in many patients, and progressively larger sets of symptoms that co-occurred among a few patients. Conclusions: These results suggest that can -cer symptoms co-occur in a nested pattern as opposed to distinct clusters, thereby demonstrating the value of exploratory network analyses to reveal complex relationships between patients and symptoms. The research also extends methods for exploring symptom co-occurrence, including methods for quanti -fying the degree of symptom overlap and for examining nested co-occurrence in co-occurrence data. Finally, the analysis also suggested implications for the design of systems that assist in symptom assessment and management. The main limitation of the study was that only one dataset was considered, and future studies should attempt to replicate the results in new data.

Original languageEnglish (US)
Pages (from-to)581-591
Number of pages11
JournalMethods of Information in Medicine
Volume49
Issue number6
DOIs
StatePublished - 2010

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Cluster Analysis
Neoplasms
Symptom Assessment
Statistical Factor Analysis
Research
Datasets

Keywords

  • Cooccurrence of cancer symptoms
  • Network visualization and analysis
  • Symptom management

ASJC Scopus subject areas

  • Health Informatics
  • Health Information Management
  • Advanced and Specialized Nursing

Cite this

The nested structure of cancer symptoms : Implications for analyzing co-occurrence and managing symptoms. / Bhavnani, Suresh; Bellala, G.; Ganesan, A.; Krishna, R.; Saxman, P.; Scott, C.; Silveira, M.; Given, C.

In: Methods of Information in Medicine, Vol. 49, No. 6, 2010, p. 581-591.

Research output: Contribution to journalArticle

Bhavnani, S, Bellala, G, Ganesan, A, Krishna, R, Saxman, P, Scott, C, Silveira, M & Given, C 2010, 'The nested structure of cancer symptoms: Implications for analyzing co-occurrence and managing symptoms', Methods of Information in Medicine, vol. 49, no. 6, pp. 581-591. https://doi.org/10.3414/ME09-01-0083
Bhavnani, Suresh ; Bellala, G. ; Ganesan, A. ; Krishna, R. ; Saxman, P. ; Scott, C. ; Silveira, M. ; Given, C. / The nested structure of cancer symptoms : Implications for analyzing co-occurrence and managing symptoms. In: Methods of Information in Medicine. 2010 ; Vol. 49, No. 6. pp. 581-591.
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