Application of knowledge graphs for clinical monitoring of the treatment process

Patient condition monitoring is essential at every stage of the treatment and diagnostic process. During treatment, the key tasks of monitoring include evaluating the effectiveness of treatment strategies and ensuring safety, specifically by avoiding complications and adverse effects from medications, procedures, or their combinations. To enhance doctor-patient interactions and improve treatment quality, intelligent monitoring tools are needed. These tools must use patient data and formalized knowledge of the subject area to identify which observations, at specific times, can indicate any deviation of the patient's condition from expected outcomes.This study aims to develop an ontology that formalizes the knowledge necessary for selecting and explaining monitoring parameters throughout treatment. Key concept relationships that address the challenges of monitoring patient conditions during treatment are identified, leading to an ontological graph for a specific class of medical monitoring issues. The study describes a method for building knowledge graphs applicable to various diseases and a reasoning process to identify unexpected conditions. A process of reasoning and the resulting recommendation is presented. This approach establishes a foundation for a decision support system tailored to this class of problems, where monitoring parameters can be adapted according to treatment stage, patient condition, and individual characteristics.