The Decision Support for Health (DSH) Group conducts research on health ICT solutions, focusing on data analytics solutions, which combine information from health sensors, electronic health records and other information sources. Our technical research areas include mobile health, health monitoring technologies, data analysis and interpretation and decision support systems. Current application areas include for example decision support for intensive care and remote care of chronic diseases such as Parkinson’s disease. We also do research and development in health ICT standards and medical device regulations. Our research is done in close collaboration with health care professionals and companies. Our teaching focuses on health ICT (especially data analysis, and data-driven decision support, including AI and ML) and regulatory aspects of it.
One of our research focuses has been to build models of people’s everyday health behaviors, predict the behaviors and to find new information on causal relations between health behaviors and outcomes. We have analyzed and combined several data sets from a variety of health data repositories owned by the Finnish and international organizations and companies, with whom we collaborate. We have published several articles e.g. from the areas of alcohol effects on sleep quality, long-term variation patterns of weight, stress-recovery patterns in the working population.
In the Digital Health Revolution project we have researched the human-centric personal health data management, data privacy and re-use. We have been developing and implementing a new architecture for health data management utilizing the MyData principles. Linked to the project, also a TUT spin-off company focusing on wellness data integration was created.
We have conducted research on wearable sensors, with special focus on improving the accuracy of wearable optical heart rate measurement systems.
We have also researched the area of technology-assisted health coaching and developed in TEKES/Business Finland and EU projects a web-based tool health and wellness coaching targeted for preventive health care. The results of the project have been commercialized through the PHI team spin-off company Movendos Oy.
A recent project was EU H2020 project “DigiNewB” where we developed conducting machine learning base solutions for the sepsis prediction of prematurely born babies in intensive care.
Our H2020 project “ENVISION” (2020-2022) focuses on machine learning-based decision support for real-time surveillance of Covid-19 patients.
In the Horizon Europe project COVend (2021-2024) we develop AI-based data analysis methods for improved management of Covid-19 patients in critical care settings.