Leveraging Topic Modeling in the Analysis of Urology Medical Reports

Medical records provide a detailed view of surgical procedures, highlights common complications, and emerging trends. The analysis of medical records is crucial for improving clinical management and optimizing resources in a urology clinic. By analyzing the surgical records of the urology department at Romolo Hospital, the main topics identified using topic modeling techniques, particularly LDA, include: details of surgical procedures, types of anesthesia, and the correlation between specific medical conditions and surgical interventions. These topics can have direct implications for improving clinical resource management and surgical planning, optimizing the quality of care provided and the effectiveness of treatments through better use of information contained in medical records. The possible research questions that this study aims could explore, include: (i) What are the most frequent themes emerging from the medical records of a urology department? (ii) How can the results of thematic analysis be used to improve operational management and enhance the quality of care? The main hypothesis is that the use of advanced models like LDA applied to unstructured data can reveal significant topics, otherwise difficult to identify through traditional statistical analyses, thus offering the opportunity to improve resource allocation and optimize treatment strategies. This study uses topic modeling to analyze the medical records of a urology clinic and identify the main themes emerging from the data. Using the Latent Dirichlet Allocation (LDA) model, relevant themes were extracted and classified, allowing the identification of patterns and trends useful for improving clinical decision-making. The results of the analysis highlighted the interconnection between certain medical conditions and surgical procedures, providing indications for better resource allocation and more effective management strategies.