Intelligent systems that build models by observing their environment and evaluating data to calculate actions optimally must systematically deal with uncertainties. Especially in healthcare, diverse and to a large extent relational data of patients, their medical history, and their examinations with measurement series and diagnoses are encountered. Associated text and image data are often to be interpreted on a semantically higher level and made usable for novel applications.
Stochastic relational models represent a crucial tool here. They are also of great importance in other areas such as robotics or the Semantic Web, since extensive data sets from web mining activities (e.g., so-called knowledge graphs) are increasingly being included in the action planning of intelligent systems. Through Stochastic Relational AI in Healthcare (StarAI), stochastics/statistics, logic, and network models are systematically combined for use in intelligent systems. For example, important problems can be solved in health care system applications. Therefore, the scalability of inference and learning algorithms for StarAI models forms an important work topic in the research department.
The human-friendly design of the interaction of intelligent systems with human actors receives special attention. An essential prerequisite here is a very high quality of system outputs or a very high appropriateness of computed actions or recommended actions of intelligent support systems based on possibly learned models. Equally important are the possibilities to explain conclusions to the user with reference to the modeling with causal reference, but also to anticipate human information needs as well as human information processing in the interaction. Both aspects are investigated in the research area (Human-aware StarAI).
StarAI uses the basic research of the Institute for Information Systems (IFIS) at the University of Lübeck, led by Prof. Dr. Ralf Möller.
IFIS projects with StarAI reference