What is Cambridge Adjutorium?
Cambridge Adjutorium is a prediction system driven by a state-of-the art machine learning model.
The system was developed by a multidisciplinary team of machine learning experts led by Professor Mihaela van der Schaar, John Plummer Professor of Machine Learning and AI in Medicine at the University of Cambridge.
How was Cambridge Adjutorium developed?
Cambridge Adjutorium was initially developed as a prognostication tool for cardiovascular disease, but from the outset was created for use with a broad range of diseases and conditions. It was since then validated for cystic fibrosis and breast cancer.
Training the system on a depersonalised COVID-19 patient dataset provided by Public Health England has shown that its predictive accuracy far surpasses existing state-of-the-art techniques.
How can Cambridge Adjutorium assist in the response to COVID-19?
Cambridge Adjutorium can provide aggregated predictions for hospitals, which could significantly help improve capacity planning for healthcare systems in response to COVID-19.
The system uses its underlying predictive models to provide accurate near-term projections of the likely demand on hospital resources such as ICU beds and ventilators. These projections are shown to healthcare decision-makers in an easy-to-interpret and actionable format
Software (hosted on BitBucket)
You can find Adjutorium here.
Professor van der Schaar’s LinkedIn page
Recent COVID-19 updates from Professor Mihaela van der Schaar
[Video] “Using machine learning and PHE data to help hospitals cope with COVID-19” (ELLIS event presentation by Professor van der Schaar, April 1, 2020)
Cambridge Adjutorium technical documentation
[Video] “Machine learning and data science for medicine: a vision, some progress and opportunities” (Turing lecture by Professor van der Schaar, 2018)
Research papers on Cambridge Adjutorium/AutoPrognosis
A. M. Alaa, M. van der Schaar, “AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning,” ICML, 2018.
A. M. Alaa, M. van der Schaar, “Prognostication and Risk Factors for Cystic Fibrosis via Automated Machine Learning,” Scientific Reports, 2018.
A. M. Alaa, T. Bolton, E. Di Angelantonio, J. H. F. Rudd, M. van der Schaar, “Cardiovascular Disease Risk Prediction using Automated Machine Learning: A Prospective Study of 423,604 UK Biobank Participants,” PloS One, 2019