Data-driven research for personalizing diabetes care: Towards precision diabetes medicine

Precision diabetes medicine (PDM) is a relatively new research field. It aims to tailor the clinical management of diabetes to an individual or smaller subgroup level. Contrary to the traditional “one-size-fits-all” approach, PDM strives to increase the efficiency and accuracy of diabetes care by personalizing care pathways. This thesis explored the potential value of different data sources (population surveys, electronic health records, clinical text, and genetic data) and advanced analytics (machine learning and biocomputing) in PDM. Findings underscore the central roles of both different data sources and state-of-the-art analytics in realizing the goal of PDM.