Clinical Phenotyping

Clinical phenotyping aims to characterize patients based on their observable characteristics and medical history, facilitating improved diagnosis, treatment, and research. Current research emphasizes developing robust machine learning models, including graph neural networks, recurrent neural networks (like LSTMs), and transformer-based language models, to analyze diverse data sources such as images, physiological time series, and electronic health records. These advancements enable more accurate and efficient identification of patient subgroups with similar disease progression patterns, leading to personalized medicine and improved clinical decision-making. The ultimate goal is to enhance the understanding of disease heterogeneity and improve healthcare outcomes.