Abstract: Critical care units, with their sophisticated patient technologies, generate massive amounts of heterogeneous data that include measurements, medical imagery, diagnosis transcriptions, etc. These data have the potential to improve our understanding of diseases and clinical care in general. The application of mathematical models, algorithms, and deep learning strategies to these records enables us to design data-driven clinical decision support tools for physicians and to devise population-level health policies for governmental organizations. In addition to helping in the study of rare disease conditions, the sheer magnitude of the available datasets will facilitate the application of advanced algorithmic techniques and prospective bedside decision support
tools.

Speaker: Pan Hui

Affiliation: Professor of Computer Science, University of Helsinki

Place of Seminar: Lecture Hall Exactum D122, University of Helsinki