Événement

Clinical research has never been more active and diverse than it is at this moment. Research efforts span national and cultural borders and broad online dissemination of results makes insights available at a global scale with ever decreasing latency. In the face of these developments, individual researchers and practitioners are confronted with a seemingly intractable amount of material (approximately 1 Million scholarly articles are newly published in the life sciences each year). While highly trained human experts excel at making precision diagnoses, coverage, especially for uncommon conditions can be greatly improved.

In this talk, we will discuss a range of (deep) machine learning techniques that provide automatic clinical decision support on the basis of large-scale data collections. The sepaker will present early and ongoing work on a) Predictive assistants in post-operative care of cardiac surgery patients, that serve as early warning systems in case of undesirable and dangerous complications. b) Automatic summarization of individually long patient records to obtain concise and topically targeted summaries for physicians. c) Data-driven diagnosis of rare diseases that individually occur too

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