The document discusses medical data and text mining techniques used to link diseases, drugs, and adverse reactions using structured and unstructured data from Danish healthcare registries and electronic health records. It describes analyzing registry data containing information on 6.2 million patients and 119 million diagnoses to study diagnosis trajectories, comorbidities, and confounding factors. It also discusses using named entity recognition, dictionaries of medical terms, and rule-based systems to extract information from free-text clinical notes written in Danish to identify adverse drug reactions and new relationships between drugs and medical conditions. The goal is to advance pharmacovigilance by supplementing spontaneous reports of adverse events with information extracted from extensive real-world healthcare data sources.