Integration of hybrid knowledge graph models for real-time decision support in emergency medical care

In today’s technological world, data collection, preservation and transfer has become one of the most important means of means of interpreting information and gaining knowledge from it. Particularly in modern medicine, medical data, which is collected through various technological means, such as smartwatches, tablets, medical devices and through the input of medical personnel into various software solutions, are essential for providing effective and patient-oriented medical treatment. However, this life-saving information is fragmented across different platforms and areas (homes, hospitals, doctors) and cannot be consolidated for further treatment. This lack of access to important external knowledge sources, such as clinical trials and cutting-edge research, leads to incomplete utilization of the available potential. This data could support medical staff in providing a coherent and rapid basis for decision-making based on personal data. This plays a particularly important role in rescue operations. In the short time available to the rescue service, the new knowledge about the situation, the patient Medical vital signs such as blood pressure, ECG and heart rate help to classify the patient on site and treat them correctly. Technical solutions could help here by using hybrid knowledge models to bundle medical knowledge and use artificial intelligence to provide support in decision-making for rescue personnel.
This thesis deals with the design of an innovative hybrid knowledge graph model for the integration of (non-) medical medical knowledge into a knowledge graph developed for medicine. The aim is to use intelligent knowledge fusion methods to integrate different knowledge sources (e.g. historical data, vital parameters), to enable more efficient, evidence-based patient treatment to make them usable and thus reduce the complexity of combining them. In the long term, the digital twin is to be used as a knowledge model, to combine knowledge streams and formulate new findings (with the help of DT services). This can initiate a knowledge reform in medicine medical knowledge, whereby distributed medical expertise accumulates added value for patients patients, medical staff and society.