Details of Grant 

EPSRC Reference:	EP/Y003527/1
Title:	Combining Mechanistic Modelling with Machine Learning for Diagnosis of Acute Respiratory Distress Syndrome
Principal Investigator:	Saffaran, Dr S
Other Investigators:
Researcher Co-Investigators:
Project Partners:	Bayer AG RWTH Aachen University
Department:	Sch of Engineering
Organisation:	University of Warwick
Scheme:	Standard Research - NR1
Starts:	01 March 2024	Ends:	28 February 2026	Value (£):	137,662
EPSRC Research Topic Classifications:	Artificial Intelligence Information & Knowledge Mgmt
EPSRC Industrial Sector Classifications:	Information Technologies
Related Grants:
Panel History:	Panel Date Panel Name Outcome 17 May 2023 ECR International Collaboration Grants Panel 1 Announced
Summary on Grant Application Form
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 acute respiratory distress syndrome (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. I propose to establish a collaboration with Prof Andreas Schuppert at Aachen University and Dr Jörg Lippert at Bayer Healthcare in Germany that will give me access to large-scale patient data and internationally leading expertise in applying machine learning to real clinical problems. As noted recently by leading medical researchers in the journal Intensive Care Medicine, "Artificial Intelligence approaches such as machine learning may assist in identification of patients at risk of or fulfilling diagnostic criteria for ARDS, although this technology is not yet ready for clinical implementation". In ARDS, patient outcomes are poor, while hospital costs are huge - this collaboration will make breakthroughs in the clinical applicability of digital technologies for the earlier identification of ARDS, improving treatment of patients and reducing costs to healthcare providers.
Key Findings
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Potential use in non-academic contexts
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Impacts
Description This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk Summary Date Materialised
Sectors submitted by the Researcher
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Project URL:
Further Information:
Organisation Website:	http://www.warwick.ac.uk