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Project

Interpretable predictive models for biomedical applications with structured outputs

In Intensive Care Medicine, bleeding edge technology is constantly applied to treat patients. In particular, it prioritizes critically ill patients, as they are suffering from life threatening conditions which may cause mortality or severe consequences. In order to improve their chances, in parallel to the technological advance, we will apply Machine Learning methods in this context. More specifically, we will employ Semi-Supervised and Active Learning to provide a better understanding of their state. By perform predictive modelling of critically ill patients, we intend to address this field from a different perspective, and consequently contributions in both Artificial Intelligence and Medicine.

Date:16 Oct 2018 →  14 Nov 2022
Keywords:Machine Learning, Intensive Care Medicine, Semi-Supervised Learning, Active Learning
Disciplines:Laboratory medicine, Palliative care and end-of-life care, Regenerative medicine, Other basic sciences, Other health sciences, Nursing, Other paramedical sciences, Other translational sciences, Other medical and health sciences
Project type:PhD project