Personalized Health Technology
Group leader: Jakob E. Bardram
Personalized Health Technology is basic to creating the 21st century healthcare delivery model which is participatory, personalized, predictive, and preventive.
The section works with a wide range of both hardware and software technologies, including wearable medical devices, biomedical signal processing, brain-computer interfaces, human-computer interfaces, artificial intelligence, machine learning, mobile & wearable sensing, personalized health technology, telemedicine, remote health monitoring, clinical support systems, and large-scale health data science infrastructures for collection, management, and analysis of health and well-being data.
The section addresses advances in diagnostics, treatment, and monitoring of a wide range of health and well-being domains, including psychiatry (e.g., depression and bipolar disorders), cognitive declined (e.g., dementia), neurological disorders (e.g., epilepsy and Parkinson's), cardiovascular diseases, and diabetes.
The group works within a set of core computer science research topics, builds and supports a set of research software technologies and applications, and is involved in a wide range of research projects.
The group's research is focused on what we have chosen to call "Personalized Health Technology" , but this line of research has also be labeled as Pervasive Healthcare [1,2], Personal Health Informatics, Digital Health, Mobile Health (mHealth), or Health IoT (Internet-of-Things). These names overlap and more or less refers to the same. Our definition of personalized health technology is;
"...a generic class of pervasive computing technologies that uses personal mobile devices and back-end servers for health- and behavior-related data sampling, processing, visualization, and feedback." [3, p. 70]
A very generic software architecture for such system are shown below (from ).
The group works in a set of core computer science research topics as well as in more health-application oriented research topics, including:
- Ubiquitous Computing, including Mobile and Wearable Sensing and Context-aware Computing
- Distributed Software Architectures for data collection, management, security, and privacy
- Feature Modelling and Machine Learning for behavior and disease modelling
- Human-Computer Interaction for the design of easy-to-use personal health technology
- Health-specific application development for e.g. psychiatry, neurology, diabetes, cardio-vascular diseases, and cancer.
The group builds and maintains a set of software tools for own and general use. Most of this is open source and is available from the CACHET github. These tools include:
The groups is involved in a range of research projects, which all takes place as part of the Copenhagen Center for Health Technology (CACHET), including:
- RADMIS – Reducing the rate and duration of readmission among patients with unipolar and bipolar disorder using smartphone-based monitoring and treatment.
- REACH – Responsive Engagement of the Elderly promoting Activity and Customized Healthcare
- REAFEL – Reaching the Frail Elderly Patient for optimizing diagnosis of atrial fibrillation
- BHRP – Biometric Healthcare Research Platform for research in psychiatric and neurological diseases using sensor technologies
- Phy-Psy Trial – A cluster randomised, parallel-group, 5-year trial of coordinated, co-produced care to reduce the excess mortality of patients with severe mental illness by improving the treatment of their comorbid physical conditions.
- TEAM – Technology Enabled Mental Health for Young People
- Bardram, J. E. (2008). Pervasive healthcare as a scientific discipline. Methods of information in medicine, 47(03), 178-185.
- Arnrich, B., Mayora, O., Bardram, J., & Tröster, G. (2010). Pervasive healthcare. Methods of information in medicine, 49(01), 67-73.
- Bardram, J. E., & Frost, M. (2016). The Personal Health Technology Design Space. IEEE Pervasive Computing, 15(2), 70-78.