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

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 clinical rules and reveal risk factor synergy. RESULTS: Four distinct phenotypes emerged: Cluster 1 (28.4%, term jaundice) had the lowest failure (20.5%); Cluster 2 (25.5%, preterm males with respiratory morbidity) the highest (48.2%); Cluster 3 (30.6%, preterm females) intermediate (38.0%); Cluster 4 (15.4%, term respiratory/infectious) moderate (24.6%) (all P < 0.001). The decision tree generated simple bedside rules (e.g., males with weight < 1.795 kg → 83.3% failure). Co-occurrence network revealed a tightly connected core of respiratory disorders, infection, and preterm/low birth weight (Jaccard 0.31-0.46), while jaundice was isolated. CONCLUSIONS: Unsupervised clustering identified ~ ~ four neonatal phenotypes ~ ~ four distinct risk co-occurrence patterns in a high-risk neonatal unit cohort. Complementary decision tree and network analyses provided clinically actionable rules and exposed synergistic risk factor patterns that logistic regression could not capture. These findings support pattern-based risk stratification as a valuable complement to variable-centered methods.