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.
In just two years, the researchers have developed technology that has moved from producing ML-based predictions of lung failure in CF patients using a snapshot of patient data – itself a remarkable improvement on the previous state of the art – to dynamic predictions of individual disease trajectories, predictions of competing health risks and comorbidities, ‘temporal clustering’ with past patients, and much more.
The researchers are presenting three of their new ML technologies this week at the North American Cystic Fibrosis Conference 2020. In-depth details of the technologies and their potential implications are available on the CCAIM website.
The tools developed by the Cambridge researchers represent astonishing progress in a very short time, and reveal the power of ML methods to tackle the remaining mysteries of common chronic illnesses and provide highly precise predictions of patient-specific health outcomes of unprecedented accuracy. What’s more, such techniques can be readily applied to other chronic diseases.
Image: Blue and Brown Anatomical Lung Wall Decor
Credit: Hey Paul Studios
Reproduced courtesy of the University of Cambridge