Patient Stratification

Patient stratification aims to divide patients into subgroups based on shared characteristics, improving treatment effectiveness and clinical trial design. Current research focuses on developing robust algorithms, including unsupervised methods like those employing graph neural networks and recurrent neural networks, to analyze diverse data sources such as omics data, echocardiograms, and electronic health records, even with incomplete or noisy information. This work addresses challenges like data integration, handling trajectory bias, and ensuring model stability and compatibility with clinician expectations, ultimately aiming to improve the precision and personalization of healthcare.