Expanding Medical Knowledge: A Clinical Decision Support System Utilizing Literature

Clinical decision support systems (CDSS) are knowledge-driven tools that have the potential to improve diagnosis and decision-making for clinicians. However, no clinical decision model is infallible. Therefore, CDSS should enable clinicians to validate each decision recommendation and reject any incorrect ones while selecting the right ones. Although previous studies have aimed to explain the arguments behind each decision candidate, these arguments are often drawn from a knowledge base that is modeled from clinical guidelines and may not always be up-to-date with the latest medical research.To address this issue, we propose a different approach that provides accurate and relevant scientific evidence from the biomedical literature. Our proposed knowledge extension engine uses the BioBERT tool to efficiently identify clinical trial reports based on a range of clinical questions. This feature enables the system to identify clinical trials that are relevant to diagnostic or treatment hypotheses. Furthermore, the knowledge extension engine can extract essential information from clinical trial summaries, such as patient populations, interventions, and outcomes. This capability allows clinicians to quickly identify matches between clinical questions and clinical trials, and understand key elements of clinical trials without extensive reading.Additionally, we have designed a knowledge modeling approach that facilitates the rapid updating of the knowledge base, enabling domain experts to quickly update the knowledge base based on the latest literature provided by the system. By following this approach, we aim to provide clinicians with up-to-date and accurate information to improve the effectiveness of CDSS in clinical decision-making.