Medical Data Analysis
In joint projects with Fraunhofer IGD (IVA), GRIS explicitly conducts research into the analysis of medical data since 2012. Together with various experts from the medical domain, GRIS has, e.g., gained expertise in the longitudinal forecast of diseases, in the exploration of cause-effect scenarios regarding clinical treatment, in the visualization of large collections of patient histories as well as in the interactive definition of meaningful patient cohorts (stratification). Driving fields of research that contributed to the competence in medical data analysis are Information Visualization as well as Visual Analytics, both of which have an emphasis on Medical Visualization.
A second branch of medical expertise comes with the expertise in Machine Learning and Deep Learning capability in particular. At GRIS, we draw a direct connection between baseline research in Machine Learning and the medical domain. The meaningful segmentation of organs or automatic guidance for surgery or Motion Planning are two reference applications.
Medical Data Analysis explained
The analysis of medical data has many facets just like data in the medical field have. Individual sources of data include treatments, drugs, histological, clinical, follow-up, or quality-of-life data. These types of data, often referred to as ingredients of electronic health records, are, e.g., often complemented with volume data as a result of medical imaging.
Different types of techniques borrowed from Machine Learning, Data Mining, and Information Retrieval may be applied and combined to provide effective and efficient solutions for the analysis of medical and patient-related data.
Other types of stakeholders (in addition to the patient-centered approaches) involved in the medical domain are, e.g., clinics, quality management environments, and insurances.