The research group primarily focuses on investigating the associations between chronic diseases, a concept known as multimorbidity (MM). Disease development relies on numerous genetic factors as well as external factors that influence gene expression, such as environmental conditions, social status, and lifestyle. The study of MM across individuals’ lifespans and the duration within each MM category is commonly referred to as MM trajectories or disease trajectories. Utilizing artificial intelligence and bioinformatics, the group aims to integrate diverse data types through algorithms to personalize health recommendations more effectively. Consequently, the group’s objectives include the identification of MM clusters, determination of their trajectories, and analysis of the influencing factors. These objectives will be pursued by leveraging electronic medical records, genetic and environmental data, lifestyle and social information, using advanced informatics techniques within population-based cohorts connected to electronic health records.
