Analyses over 60 clinical variables including vital signs, blood biochemistry, respiratory physiology and laboratory results from a patient’s electronic health record, to predict their risk of three clinical outcomes; admission to intensive care, need for invasive mechanical ventilation and in-hospital mortality.
A clinician is provided with the individual patient risk score and a certainty level for each risk prediction, to inform clinical judgement.
SYNE-COV is provided as a real time, scalable API for seamless integration into existing hospital systems and dashboards, meaning doctors can view the algorithm results alongside other patient record data, with no need for a separate application.
Risk scores provided in seconds to augment clinical decision making.
SYNE-COV outperforms traditional risk prediction models, providing a deeper, more patient-based indication of three specific outcomes.
Patient specific risk scores enable more risk prediction and expedited decision making which may lead to more efficient use of hospital resources and shorter length of stay.
UKCA marked, registered with MHRA as Class 1 Medical Device and FDA EUA pending.
Provided within a Microsoft Azure cloud environment, enabling scalable implementation and strict adherence to the highest standards of privacy and information governance.
SYNE-COV is designed to work on data that do not contain personal identifiable information. Data are stored only temporarily and deleted after each use.
Early risk assessment for COVID-19patients from emergency department data using machine learning
Heldt, F.S., Vizcaychipi, M.P.,Peacock, S. et al. Sci Rep 11, 4200(2021).
Risk factors for clinical progression in patients with COVID-19: a retrospective study of electronic health record data in the UnitedKingdom
Fletcher R.A. et al. medRxiv2020.05.11.20093096.
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