There are five collaborating partners in the 2-year D4Health- project  funded by Academy of Finland (funding period 1.1.2016-31.12.2017)

Consortium leader: Juho Rousu, Department of Computer Science, Aalto University (CS/Aalto)

Partners: Samuel Kaski, Helsinki Institute for Information Technology HIIT, Aalto University (HIIT/Aalto) Giulio Jacucci, Helsinki Institute for Information Technology, University of Helsinki (HIIT/HY), Tero Aittokallio, Institute for Molecular Medicine Finland, University of Helsinki (FIMM), Raimo Sepponen, Department of Electrical Engineering and Automation, Aalto University (EEA/Aalto)

SUMMARY: 

We propose to develop a new digital healthcare methodology for interfacing doctors to medical records and measurement data about patients, to greatly improve diagnostics and therapy decisions, thus improving the quality of healthcare while reducing costs. Machine learning from large measurement data will be used to give predictions, which are refined through user feedback, and connected to other relevant data and medical records through visualizations of an interactive intent modelling-based retrieval system. This general extensible paradigm will be exemplified in case studies. The use of open and big data is in the heart of this project: data-driven use and integration of large medical records, patient monitoring and open biomedical data is novel both in Finland and internationally and presents a huge opportunity. This proposal brings together a new, necessary, and unique constellation of expertise The present consortium brings together in an unique way the multidisciplinary expertise required for developing digital health methodologies and tools that go beyond the state-of-the-art, namely machine learning, biomedical research, health care technology as well as human- computer interaction. The collaborators in public healthcare and health technology industry provide an active link towards the practical use and interactive testing of the developed methods and tools. In the project large real-world medical datasets are analysed in three medical case studies, in combination of open biomedical databases.