Patient Phenotype

Patient phenotyping aims to identify and characterize distinct subgroups of patients based on shared clinical characteristics, improving diagnosis, treatment, and prognosis. Current research utilizes diverse approaches, including deep learning models (e.g., transformers, neural networks) for analyzing complex datasets like electronic health records and genomic data, and employing techniques like topic modeling and clustering to identify meaningful patient subgroups. These efforts are significantly impacting healthcare by enabling more precise disease classification, personalized medicine strategies, and improved prediction of patient outcomes, such as mortality and length of stay. The development of robust ontologies and improved methods for handling incomplete or heterogeneous data are key areas of ongoing focus.