Clustering based on unsupervised binary trees to define subgroups of cancer patients according to symptom severity in cancer

Clustering based on unsupervised binary trees to define subgroups of cancer patients according to... Background Studies have suggested that clinicians do not feel comfortable with the interpretation of symptom severity, functional status, and quality of life (QoL). Implementation strategies of these types of measurements in clinical practice imply that consensual norms and guidelines regarding data interpretation are available. The aim of this study was to define subgroups of patients according to the levels of symptom severity using a method of interpretable clustering that uses unsu- pervised binary trees. Methods The patients were classified using a top-down hierarchical method: Clustering using Unsupervised Binary Trees (CUBT). We considered a three-group structure: “high”, “moderate”, and “low” level of symptom severity. The clustering tree was based on three stages using the 9-symptom scale scores of the EORTC QLQ-C30: a maximal tree was first devel- oped by applying a recursive partitioning algorithm; the tree was then pruned using a criterion of minimal dissimilarity; finally, the most similar clusters were joined together. Inter-cluster comparisons were performed to test the sample partition and QoL data. Results Two hundred thirty-five patients with different types of cancer were included. The three-cluster structure classified 143 patients with “low”, 46 with “moderate”, and 46 with “high” levels of symptom severity. This partition was explained http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality of Life Research Springer Journals

Clustering based on unsupervised binary trees to define subgroups of cancer patients according to symptom severity in cancer

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Publisher
Springer International Publishing
Copyright
Copyright © 2017 by Springer International Publishing AG, part of Springer Nature
Subject
Medicine & Public Health; Quality of Life Research; Sociology, general; Public Health; Quality of Life Research
ISSN
0962-9343
eISSN
1573-2649
D.O.I.
10.1007/s11136-017-1760-9
Publisher site
See Article on Publisher Site

Abstract

Background Studies have suggested that clinicians do not feel comfortable with the interpretation of symptom severity, functional status, and quality of life (QoL). Implementation strategies of these types of measurements in clinical practice imply that consensual norms and guidelines regarding data interpretation are available. The aim of this study was to define subgroups of patients according to the levels of symptom severity using a method of interpretable clustering that uses unsu- pervised binary trees. Methods The patients were classified using a top-down hierarchical method: Clustering using Unsupervised Binary Trees (CUBT). We considered a three-group structure: “high”, “moderate”, and “low” level of symptom severity. The clustering tree was based on three stages using the 9-symptom scale scores of the EORTC QLQ-C30: a maximal tree was first devel- oped by applying a recursive partitioning algorithm; the tree was then pruned using a criterion of minimal dissimilarity; finally, the most similar clusters were joined together. Inter-cluster comparisons were performed to test the sample partition and QoL data. Results Two hundred thirty-five patients with different types of cancer were included. The three-cluster structure classified 143 patients with “low”, 46 with “moderate”, and 46 with “high” levels of symptom severity. This partition was explained

Journal

Quality of Life ResearchSpringer Journals

Published: Dec 8, 2017

References

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