Machine learning comes of age in cystic fibrosis
World-leading AI technology developed by the Cambridge Centre for AI in Medicine and their colleagues offers a glimpse of the future of precision medicine, and unprecedented predictive power to clinicians caring for individuals with the life-limiting condition. 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.
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.
(Content Courtesy: https://www.cam.ac.uk/research/news/machine-learning-comes-of-age-in-cystic-fibrosis)