Stratification of Group A Streptococcal Pharyngitis Children Using Unsupervised Learning
Yoshifumi Miyagi

TL;DR
This study uses unsupervised learning to classify children with strep throat based on symptoms, aiming to improve diagnostic criteria.
Contribution
The novel use of K-modes clustering to stratify GAS pharyngitis patients by clinical symptoms.
Findings
Two distinct patient clusters were identified based on age and symptoms.
Older children with lymph node tenderness formed one cluster, while younger children with cough and runny nose formed another.
The study highlights the difficulty in distinguishing strep throat from colds in young patients.
Abstract
Background and objectives Group A Streptococcus (GAS) is the most frequent cause of bacterial pharyngitis, and it is advised to selectively use rapid antigen detection testing (RADT). Currently, the decision to perform this test is based on pediatricians' observations, but the criteria are not well-defined. Therefore, we utilized unsupervised learning to categorize patients based on the clinical manifestations of GAS pharyngitis. Our goal was to pinpoint the clinical symptoms that should prompt further examination and treatment in patients diagnosed with pharyngitis. Methods We analyzed categorical data from 305 RADT-positive patients aged three to 15 years using the K-modes clustering method. Each explanatory variable's relationship with cluster variables was statistically examined. Finally, we tested the differences between clusters for continuous variables statistically. Results…
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Taxonomy
TopicsStreptococcal Infections and Treatments · Otolaryngology and Infectious Diseases · Antimicrobial Resistance in Staphylococcus
