Medical records contain a wealth of information about a patient’s health, treatments, and clinical history; however, much of this data is recorded in free-text format, making it difficult to analyse and apply directly. Discharge summaries, which provide a detailed overview of a patient’s hospital stay including diagnoses, treatments, medications, and follow-up instructions frequently contain domain-specific terminology, abbreviations, and inconsistent grammar, further complicating automated analysis. Extracting meaningful information from these summaries can help healthcare systems better understand patient conditions, support clinical decision-making, and facilitate medical research. Various approaches have been proposed to address these challenges. The SR classifier effectively extracts fine-grained relationships, while methods such as Three-way Inter-Annotator Agreement (IAA) use annotation tools for keyword-based relation identification. Deep learning techniques, including Bi-LSTM, CRF, and CNN models, have been employed to capture sequential patterns, entity structures, and relationships between medical entities. Modern transformer-based models further enhance these capabilities by learning complex contextual representations. By combining these methods, unstructured medical text can be transformed into structured, actionable knowledge, improving patient care and enabling data-driven healthcare analytics.