Finding Groups in Obstructive Sleep Apnea Patients: A Categorical Cluster Analysis

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2018

Autores

Santos, DF (Autor) FMUP; Pedro Pereira Rodrigues (Autor) FMUP

Ata de Conferência Internacional

Título: 31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018, Karlstad, Sweden, June 18-21, 2018

Páginas: 387-392

31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018

18 June 2018 through 21 June 2018

Indexação

Scopus - 1 Citação

Outras Informações

ID Authenticus: P-00P-268

DOI: 10.1109/CBMS.2018.00074

Abstract (EN): Obstructive sleep apnea (OSA) is a significant sleep problem with various clinical presentations that have not been formally characterized. This poses critical challenges for its recognition, resulting in missed or delayed diagnosis. Recently, cluster analysis has been used in different clinical domains, particularly within numeric variables. We applied an extension of k-means to be used in categorical variables: k-modes, to identify groups of OSA patients. Demographic, physical examination, clinical history, and comorbidities characterization variables (n=46) were collected from 318 patients; missing values were all imputed with k-nearest neighbors (k-NN). Feature selection, through Chi-square test, was executed and 17 variables were inserted in cluster analysis, resulting in three clusters. Cluster 1 having an age between 65 and 90 years (54%), 78% of males, with the presence of diabetes and gastroesophageal reflux, and high OSA prevalence; Cluster 2 presented a lower percentage of OSA (46%), with middle-aged women without comorbidities, but with gastroesophageal reflux; and Cluster 3 was very similar to cluster 1, only differing in age (45-64) and comorbidities were not present. Our results suggest that there are different groups of OSA patients, creating the need to rethink the baseline characteristics of these patients before being sent to perform polysomnography (gold standard exam for diagnosis). © 2018 IEEE.

Idioma: Inglês

Tipo (Avaliação Docente): Científica