Machine Learning Applications

ICU Time series & Early Warning Systems

In intensive care units (ICUs),  physicians are facing large quantities of data from many patients, such as physiological signals, lab measurements, observation records, etc. It becomes increasingly challenging to identify the most important information for care decisions using only manpower. Our group develops Early Warning Systems (EWS) for different types of organ failure based on machine learning methods to forecast impending organ failure using both time series as well as other meta-data from the ICU of Inselspital (University Hospital Bern). [Read more ...]

Multi-Omics Data Integration (TuPro)

Single cell profiling technologies allow for the profiling of biological samples at the resolution of individual cells. Over the last decade these technologies have developed at an explosive rate [1] and we can now profile cells across several biological modalities, measuring properties such as gene or protein expression, spatial information via imaging technologies, DNA mutations, etc. These data modalities each contribute a unique perspective and a more holistic view of cellular state would be achieved by combining the information from each perspective. [Read more ...]

Medical language representation & Information Extraction from Clinical Reports

Clinical reports written by medical staff provide and record key information about patient symptoms, diseases, and treatments. Being able to automatically extract such information is of great practical relevance for various aspects of clinical practice and research involving patient data. However, very often, these reports contain little or no structured information and are composed as free text within document templates. Integrating this text information requires a representation of medical language. [Read more ...]