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Researchers have developed a machine learning algorithm that could improve clinicians’ ability to identify hospitalised patients who need intensive care.

Hospital room

The HAVEN system was developed as part of a collaboration between the University of Oxford’s Institute of Biomedical Engineering and the Nuffield Department of Clinical Neurosciences (NDCN), with support from the NIHR Oxford Biomedical Research Centre.

The findings of the study have been published in the American Journal of Respiratory and Critical Care Medicine.

'The HAVEN machine learning algorithm, using electronic patient data collected routinely by most NHS hospitals, has the potential to substantially improve our ability to detect patients who require ICU, and those for whom a timely intervention is likely to change their outcome, so enhancing the National Early Warning Score (NEWS) system currently in use across the health service,' said Professor Peter Watkinson, Associate Professor of Intensive Care Medicine at NDCN.

The HAVEN (Hospital-wide Alerting Via Electronic Noticeboard) system combines patients’ vital signs - such as blood pressure, heart rate and temperature - with their blood test results, comorbidities and frailty into a single risk score. The HAVEN score gives a more precise indication of which patients are deteriorating when compared with previously published scores.

Read the full story on the Nuffield Department of Clinical Neurosciences website

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