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iNNOCENS: data driven clinical decision support for improved neonatal care.
Analysis of patient related vital parameters generated in a continuous manner on a neonatal intensivecare department offers the opportunity to develop computational models that can predict care-relatedcomplications. This project aims to develop a machine learning model that can predict acquired braininjury of prematurity. The model can than be implemented to generate bedside visualizations in thecontext of a self-learning digital early warning system.
Date:1 May 2019 → 30 Apr 2020
Keywords:EARLY WARNING SYSTEM, DATA DRIVEN HEALTHCARE, PATIENT MONITORING DATA MINING
Disciplines:Machine learning and decision making, Neonatology