van der Schaar Lab and COVID-19
The van der Schaar Lab has played an active role in the academic and clinical response to the COVID-19 pandemic, including:
- calling upon governments to adopt proven machine learning methods and use existing data to help healthcare infrastructure respond to the pandemic;
- conducting research and statistical analysis regarding the nature of the disease and its spread, and exploring the potential impact of machine learning on clinical trials;
- developing and implementing tools such as Cambridge Adjutorium to enable clinicians to predict utilization of scarce resources, such as ventilators and ICU beds; and
- creating Policy Impact Predictor (PIP), a machine learning tool developed to guide government decision-making around measures to prevent the spread of COVID-19.
Content related to COVID-19
Cambridge Adjutorium is a prediction system created by the van der Schaar Lab. It’s driven by a state-of-the art machine learning model, and is currently being used by NHS hospitals in the UK to help manage capacity.
Here’s a guide to how Cambridge Adjutorium works:
Cambridge Adjutorium FAQ
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
Do you have a demo I can use?
You can find a working demo here on BitBucket.
(Note that the included data is synthetic to ensure that the demo runs.)
I work for a public health body, and I’d like to talk to someone about implementing Cambridge Adjtorium. How can I make that happen?
For more info on Cambridge Adjutorium, click here.
Collaborative paper published alongside Dr. Ari Ercole entitled “Between-centre differences for COVID-19 ICU mortality from early data in England“
NHS Digital press release on implementation of Cambridge Adjutorium at acute trusts in the UK: “Trials begin of machine learning system to help hospitals plan and manage COVID-19 treatment resources developed by NHS Digital and University of Cambridge“
ZDNet article (April 21) on partnership with NHS Digital: “This AI tool helps hospitals predict COVID-19 bed and ventilator demand“