The shortage of hospital beds, oxygen as well as ventilators prompted the need for this clinical decision-making tool which could assist clinicians in resource allocation during the pandemic, including the allocation of staff, beds and ventilation equipment.”ĭuring the first wave of the pandemic, Dr Daneshkhah together with Dr Abhinav Vepa, Senior House Officer in General Medicine at MKUH, reviewed and assessed 44 risk factor variables in more than 355 COVID-19 inpatients using the Bayesian Network ML method – a model used to represent knowledge about an uncertain domain. Alireza Daneshkhah, Associate Professor and Curriculum Lead in Data Science and Artificial Intelligence, Coventry University, stated: “After entering one of the greatest global catastrophes of the century, clinicians all around the world were puzzled as to what puts a person at risk from COVID-19, and whether this risk could be quantified in order to compare between individuals.
Predictive factors include t he duration of a hospital stay, the risk of developing blood clots in the lungs, the likely need for ventilator support, or the probability of possible death.įollowing the UK’s second lockdown, Coronavirus had contributed towards more than 150,000* UK deaths, whilst at the height of crisis, just under 35,000** hospital beds were occupied by COVID-19 inpatients.ĭr. Data scientists develop decision-making tool to improve treatment of COVID-19 patientsĬoventry University and Milton Keynes University Hospital (MKUH) joined forces during the pandemic to develop a tool that hopes to ease the COVID-19 burden on the NHS.ĭata scientists from Coventry University’s Centre for Computational Science and Mathematical Modelling (CSM) and MKUH used machine learning methods (ML) to support clinicians in predicting the effects of COVID-19 on each diagnosed patient involved in the study, alongside the associated risks of further ill health.