Smart4Health enables citizens to manage and bridge their own health data throughout the EU and beyond, advancing own and societal health and wellbeing.

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The project “Digital solutions made in Brandenburg | digisolBB” is a consulting project for the public sector, estimated to be run until Q2/2022. A diverse project consortium consisting of backgrounds in academia as well as consulting won the bid from the Ministry of Economic Affairs and Energy of the Land Brandenburg to outline and implement an economic development strategy in the area of Digital Health for the federal state of Brandenburg.

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Here you will find an overview of current research topics of our team. Please click on the topic to get more information

• Clinical Prediction Models (Harry Freitas Da Cruz)
• Development and evaluation of a virtual “Personal Functioning Coach” (PFC) for chronic low back pain patients (cLBP) (Sarah Wernicke)
• Digital Therapeutic and Personalized Decision Support Interventions for People with Low Back Pain: A Health Economic Perspective (Daniel Lewkowicz)
• Data-driven patient subgroup identification and outcome prediction in unspecific back pain patients (Jan Philipp Sachs) 
• Discovery of Digital Biomarker (Ariane Morassi-Sasso)
• Forecasting Mental Earthquakes (ForMe) (Dr. Hanna Drimalla)
• Machine learning on electronic health records focused on hypertension and other cardiovascular disease (Suparno Datta)
• Reacting to Risk: Designing a Personalized Health Application to Support Individuals in Disease Prevention (Tamara Slosarek)

The focus is on the following research areas: 

• Machine learning/deep learning: Developing innovative methods for diagnosing health problems/predicting health outcomes in high-dimensional, heterogeneous data
• Knowledge representation: Extracting information from disparate data sources and making information interoperable across diverse information systems
• Medical knowledge discovery and data mining: Analysis of large-scale electronic health record and clinical trials databases; Retrieval, pipelines, and characterization of disease-specific cohorts; Integration of EHR with genomic data; Data-driven identification of patient subgroups based on clinical and treatment process data
• Telemonitoring/medical sensor technologies: Extracting useful and useable information from real-time sensor technologies and internet of things
• Intelligent personal health assistance and/or clinical decision support agents: Developing artificial intelligence solutions to improve health outcomes through supporting better personal health decisions and better clinical care decisions
• Data sovereignty: Developing innovative electronic consent procedures that enable patient-/person-centric sovereignty concerning data access, sharing and use
• Privacy protection: Enhancing trust with advanced digital tools and methods for privacy protection, such as data de-identification (pseudonymization) and anonymization, or encryption