Speakers
Description
Every day, hospital professionals face the challenge of navigating through extensive patient information that is also often not available at the point of care. The complexity and volume of data, along with time pressure, can easily lead to critical information being overlooked. With the digitization of patient records, an adaptive information provision system such as CAIS.ME (Context-Aware Information System for Medical Environments) could support daily clinical work by displaying relevant information at the right time and place on lightweight AR smart glasses, thereby reducing the cognitive load placed on medical staff.
Given the importance of personalization and good usability in CAIS.ME, one of the major research areas of the project is AI-based customization of the provided data. The presentation style, content, and level of information detail of interest to the user may depend on many factors, including vital signs, medical history, laboratory values, age, other medical conditions, as well as user preferences and context.
The foundation for the envisioned multi-step frame adaptation process was laid by Tom-Maurice Schreiber in his master's thesis that focused on facilitating decision-making processes, such as finding an optimal treatment, and early detection of alarming changes in a patient's health. The main part of the proposed solution is the knowledge graph that is created on the basis of a small set of clinical guidelines and captures relationships between diseases (e.g., acute appendicitis) and observations (e.g., high white blood cell level), while also taking into account various patient characteristics (e.g., age). Realized in Neo4J as a property graph, it is queried to determine the prioritization of information that is subsequently utilized in the generation of smart glasses frames.
