Machine learning to analyse ICU data

Scientists at a new UCL centre will use tools such as machine learning to analyse intensive care data from two London hospitals to find clues that will improve the care of critically ill patients.

Researchers at CHIMERA (Collaborative Healthcare Innovation through Mathematics, EngineeRing and AI) will examine anonymised data from 40,000 patients at University College London Hospital (UCLH) and Great Ormond Street Hospital (GOSH), to develop a better understanding using mathematical modelling of how people’s physiology changes during ill health and recovery. This new understanding will in turn provide new ideas for how critically ill patients can best be cared for. 

Areas of focus are expected to include mapping Covid-19’s effect on the physiology of critically ill patients, but also looking for early warning signs for sepsis – a condition that causes one in five deaths globally – and finding new ways to spot patients who are about to deteriorate rapidly. 

CHIMERA’s partner hospitals store data collected every few seconds from monitors for patients in intensive care, such as heart rate, blood pressure, oxygen levels and temperature. Only a brief snapshot of this data is used to inform decisions around patient care at the moment. 

Researchers at CHIMERA will analyse this complex array of data using tools from data science and machine learning, and then use this to develop new mathematical models of how our body is behaving during ill health and recovery, with the aim of improving care. 

CHIMERA is an EPSRC-funded Mathematical Sciences-Healthcare Technologies Hub. Professor Rebecca Shipley is PI. The Co-Investigators are: Professor Simon Arridge, Dr Vanessa Diaz, Dr Nicholas Ovenden , Professor Mark Peters, Professor Christina Pagel, Dr Francisco Alejandro Diaz de la O, Dr Stephen Harris and Dr Samiran Ray