From clusters to clinical rules: unsupervised machine learning identifies four newborn hearing phenotypes with bedside risk stratification.

Abstract excerpt

OBJECTIVE: To identify risk patterns among high-risk newborns associated with hearing screening failure using unsupervised machine learning. STUDY DESIGN: Retrospective cohort of 447 newborns. Partition around medoids clustering (Gower distance) was applied to demographic, perinatal, and diagnostic data. CART decision tree and diagnostic co-occurrence network analyses were performed to translate phenotypes into...

Topics

• Humans
• Male
• Retrospective Studies
• Phenotype
• Unsupervised Machine Learning
• Infant, Newborn
• Female