Accurately predicting how an individual’s chronic illness is going to progress is critical to delivering better-personalised, precision medicine. Only with such insight can a clinician and patient plan optimal treatment strategies for intervention and mitigation. Yet there is an enormous challenge in accurately predicting the clinical trajectories of people for chronic health conditions such as cystic fibrosis (CF), cancer, cardiovascular disease and Alzheimer’s disease.
“Prediction problems in healthcare are fiendishly complex,” said Professor Mihaela van der Schaar, Director of the Cambridge Centre for AI in Medicine (CCAIM). “Even machine learning approaches, which deal in complexity, struggle to deliver meaningful benefits to patients and clinicians, and to medical science more broadly. Off-the-shelf machine learning solutions, so useful in many areas, simply do not cut it in predictive medicine.”
Unlock this complexity, however, and enormous healthcare gains await. That is why several teams led by Professor van der Schaar and CCAIM Co-Director Andres Floto, Professor of Respiratory Biology at the University of Cambridge and Research Director of the Cambridge Centre for Lung Infection at Royal Papworth Hospital, have developed a rapidly evolving suite of world-class machine learning (ML) approaches and tools that have successfully overcome many of the challenges.
Interstitial Idiopathic Pneumonia with Fibrosis
Brother in hospital with pulmonary fibrosis 
Salt Therapy using a CPAP machine
learning how to drive 
