TRANSFER is a pioneering project that aims to revolutionize anesthesiology through the use of artificial intelligence (AI) and machine learning (ML). At the core of TRANSFER is the research of an AI-based Clinical Decision Support System (CDSS) that operates in real time and is based on a systems medicine approach. A key strategy of TRANSFER is the integration and analysis of 300,000 multimodal perioperative data items, including biosignals, demographic information, laboratory results, and imaging data with more than 9 million data points. By using AI, TRANSFER aims to provide precise prognoses and tailored treatment recommendations during non-cardiac surgical procedures based on this data. The main focus of TRANSFER is on predicting low blood pressure (hypotension) and periods of restricted blood flow (hypoperfusion) during anesthesia for a non-cardiac surgical procedure using a novel artificial intelligence (AI)-based, real-time clinical support system. Given that postoperative mortality is the third leading cause of death in industrialized countries and that any prolonged hospital stay due to postoperative complications increases treatment costs, the urgency of such a support system is evident. This is especially true for patients with pre-existing cardiovascular diseases, which demographic changes will bring in the coming years. The DFKI is developing a suitable AI architecture and conducting tests to evaluate the performance of the approach.
Partners
- ID Informations und Dokumentation im Gesundheitswesen GmbH und Co. KGaA
- Charite - Universitätsmedizin Berlin
- Technische Universität Berlin
- SectorCon Ingenieurgesellschaft mbH
