A recent study introduces a novel approach to phenotype discovery in electronic health records (EHR) by incorporating prior clinical knowledge into unsupervised learning methods. This integration aims to improve the interpretability of complex EHR data, with a specific focus on identifying asthma sub-phenotypes. The method has been reported to be effective, highlighting its potential to enhance healthcare analytics. By leveraging prior knowledge, the approach addresses challenges in phenotype identification that traditional unsupervised techniques may face. This advancement aligns with ongoing efforts to apply machine learning in clinical settings, particularly for nuanced disease characterization. The findings underscore the promise of combining domain expertise with data-driven models to extract meaningful insights from large-scale health records. Such developments could pave the way for more personalized and precise medical interventions based on refined phenotype classifications.