ICU cockpit: computer assistance for intensive and emergency medicine

The project is aimed at initiating a fundamental development in emergency and intensive medicine – and bringing about a substantial improvement in the way diagnostics, treatment and risk management are handled in everyday clinical practice.

  • Project description (Completed research project)

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    The “ICU cockpit” project has been under development in the neurosurgery intensive care unit since 2014 in partnership with ETH Zurich, IBM Research Rüschlikon and industry partner Supercomputing Systems. State-of-the-art information technology captures data from numerous pieces of medical equipment in real time and at a resolution up to 200 Hertz. The data are time-synchronised and encoded prior to storage. The task now is to enable the ICU cockpit to identify and eliminate artefacts in the biosignals. We are also developing algorithms for the early detection of epileptic seizures and secondary impairment of cerebral perfusion.

  • Background

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    Medical knowledge has a half-life of just a few years. Doctors struggle to keep up with the explosion of information. In addition, the volume of data per patient is increasing exponentially in the field of personalised healthcare. In intensive and emergency medicine, the situation is compounded by real-time signals from multiple sensors in and on the body. In an emergency situation, in particular, it is not possible to integrate this flood of information rapidly into the decision-making process.

  • Aim

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    In an approach combining data mining, machine learning and artificial intelligence, the enormous volumes of stored data will be used to model complex pathophysiological situations for the purpose of developing algorithms for early warning systems and therapeutic recommendations.

  • Results

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    With the present project, data from patient history, brain imaging, laboratory values and biosensors as well as video monitoring could be systematically collected in more than 300 patients. Based on these data, algorithms for the following three use cases could be developed:

    After validation in further data sets, the algorithms will be implemented into software running at the bedside in daily clinical practice. The decision support systems may improve therapy in neurocritical care and lead to better patient outcome in the future.

  • Relevance/application

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    The reduction of signal artefacts and false alarms increases patient safety in the intensive care unit. Identifying risk constellations and predicting critical complications permits earlier therapeutic intervention. Therapeutic decisions, which are often made on an empirical basis, are supported by data analysis and state-of-the-art medical knowledge. Real-time data flow analysis generates new pathophysiological information – and new knowledge thanks to self-learning systems.

  • Original title

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    ICU-Cockpit: IT platform for multimodal patient monitoring and therapy support in intensive care and emergency medicine