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: Alexander Ilin

Affiliation: Professor of Computer Science, Aalto University

Place of Seminar: Seminar Room T6, Konemiehentie 2, Aalto University