CORONET, a tool created by our Research Software Engineers (RSEs), has recently been featured in the Journal of Clinical Oncology (JCO): Clinical Cancer Informatics. Find out more about the paper’s findings which explain how the Machine Learning model was created and presented to the healthcare community.

A tool created in conjunction with the Digital Experimental Cancer Medicine Team to support decisions regarding hospital admissions or discharge in cancer patients presenting with symptoms of COVID-19 has been selected for a NIHR Clinical Research Network award.

Research Software Engineers, Peter Crowther and Ann Gledson, have been working with the University Department of Mathematics and the London School of Hygiene and Tropical Medicine on improving their simulation model of contact tracing, which can be used to predict and explain the effectiveness of different strategies in reducing the spread of SARS-CoV-2

In January 2021, our Research Software Engineers worked with the Digital Experimental Cancer Medicine Team (dECMT) to create a tool to support decisions regarding hospital admissions or discharge in cancer patients presenting with symptoms of COVID-19. The tool has been so successful that multiple improvements are now planned.

Cancer patients are at increased risk of severe COVID-19. Decision-making tools for hospital admission, severity prediction and increased monitoring for early intervention are critical. The objective of this project was to identify features of COVID-19 in cancer patients predicting severe disease and build a decision-support online tool - COVID-19 Risk in Oncology Evaluation Tool (CORONET).