Contrastive Machine Learning to Quantify Hypertensive Multiorgan Damage and Identify New Disease Phenotypes: A Multinational Multimodal Study

The authors aimed to develop a novel machine learning approach to quantify multiorgan damage associated with hypertension and identify distinct disease phenotypes. By analyzing data from over 27,000 participants, they created a global organ damage score (HyperScore) and identified six hypertensive disease phenotypes, demonstrating the model's effectiveness in predicting organ-specific disease progression and survival outcomes. This study suggests that machine learning can enhance hypertension assessment and enable personalized risk management strategies.