Digital Phenotyping

Digital phenotyping uses digital data from various sources, such as wearables, smartphones, and electronic health records (EHRs), to automatically characterize individuals' traits and conditions. Current research focuses on developing and validating machine learning models, including large language models (LLMs) and deep learning architectures, to extract meaningful phenotypes from diverse data types, often integrating multiple data modalities for improved accuracy. This approach holds significant promise for improving healthcare by enabling earlier disease detection, personalized medicine, and more efficient clinical trials through the creation of more precise and readily available patient cohorts.