Sina's project is entitled "Combining mechanistic modelling with machine learning for diagnosis of Acute Respiratory Disease Syndrome". The leading medical researchers in the journal of Intensive Care Medicine recently highlighted, "artificial intelligence approaches such as machine learning may assist in identification of patients at risk of or fulfilling diagnostic criteria for acute respiratory distress syndrome (ARDS), although this technology is not yet ready for clinical implementation." This project will combine large-scale electronic patient data, artificial intelligence algorithms, and mechanistic mathematical models, to develop systems that can improve the diagnosis, and hence treatment, of critically ill patients with ARDS. The key idea is to use mechanistic virtual patient models as “filters” to extract relevant medical information on individual patients, significantly reducing biases introduced by machine learning on heterogeneous datasets, and allowing improved discovery of patient cohorts driven exclusively by medical conditions.
Speaking of the opportunity, Dr Saffaran said: "This award will facilitate collaboration between my team and world-leading researchers in the fields of machine learning and data science. Given the poor outcomes and substantial hospital costs associated with ARDS, this partnership holds the promise of making breakthroughs in the clinical applicability of digital technologies for the earlier identification of ARDS, improving treatment of patients and reducing costs to healthcare providers."
Alex's award is for the project "Parameter identification with optimal experimental design for engineering biology". Engineering biological systems for next generation therapeutics or for sustainable chemical manufacture is currently a time-intensive and expensive process due to unintended interactions between host organism and the engineered process which are neglected during the engineering process. This project will enable us to develop new mathematical frameworks which account for these processes and in the long term will enable the creation methods to engineer ‘host friendly’ processes with improved performance.
Dr Darlington said: "This award will enable us to work alongside world leading experts in parameter identification, as well as get into the lab with leading experimental synthetic biologists to develop a new set of design methods for engineering cellular production platforms with applications from chemical production to biomedicine."
Further information on the award scheme can be found on the UKRI website: https://www.ukri.org/opportunity/early-career-researcher-international-collaboration-grants/