A new grant from the government’s Engineering and Physical Sciences Research Council will help a team of experts at Imperial College London train algorithms to solve healthcare, manufacturing and energy problems.
The programme will be known as PREMIERE, which stands for Predictive Modelling with Quantification of Uncertainty for Multiphase Systems, with the project running for five years.
It’s expected that the programme, based on machine learning-powered computer simulations, will boost decision-making, safety management, improve supply chain design and help reduce carbon emissions.
In a healthcare setting, data-driven algorithms could be used to inform personalised patient care and help to predict certain conditions that are hard to diagnose. Imperial will work alongside clinicians from the University of Birmingham to collect information on acute compartment syndrome.
This is apparently a life-threatening condition that is often seen in road traffic accidents and which is hard to diagnose correctly. Incorrect diagnoses could result in amputation or death. But algorithms could be used to diagnose patients based on their symptoms and those of patients previously diagnosed, which could potentially lead to more accurate diagnoses.
“Enabling early diagnosis in the presence of uncertainty requires improved understanding of how symptoms relate to correct diagnoses. Data-driven personalised therapies could help address cases like this and bring a wealth of new personalised medicine applications,” professor Omar Matar, principal investigator with Imperial’s Department of Chemical Engineering, said.
This comes after the government announced that £250 million will be invested in artificial intelligence technology for the NHS, with the aim being to revolutionise healthcare in this country. Boris Johnson claimed that the technology would help with early detection of diseases like cancer and dementia, as well as implementing new treatments for patients.
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