van der Schaar Lab


COVID-19: Our impact in short

From the very beginning of the response to COVID-19, the van der Schaar Lab was at the forefront of using AI and ML to fight the pandemic. Here are our main accomplishments:

Our Lab presented the world’s first concrete solution on how to leverage ML and AI against COVID-19 in March 2020: Responding to COVID-19 with ML

In Spring 2020, we developed the Cambridge Adjutorium for COVID-19 in partnership with the NHS as the first ML solution to concretely help with COVID-19 by forecasting the need for scarce resources in hospitals such as ICU beds and ventilators: Partnering with NHS Digital and Public Health England and our paper

The van der Schaar Lab was the first to produce worldwide research highlighting COVID-19’s impact on minorities and under-represented communities in Brazil and the UK as early as May 2020.

Analysing possible government responses, we introduced the first Policy Impact Predictor for non-pharmaceutical interventions against COVID-19.

Aside from policy, the van der Schaar Lab also leveraged its expertise to guide pharmaceutical companies on streamlining clinical trials during the pandemic in August 2020: Machine learning for clinical trials in the era of COVID-19

In conjunction with clinicians at the University of Amsterdam Medical Centre, we developed the first solution to find personalised doses of dexamethasone for patients suffering from COVID-19: our paper

Our 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:

  • providing the world’s first concrete guidance of how machine learning & AI can help healthcare systems in the fight against Covid-19;
  • developing and implementing Cambridge Adjutorium for COVID-19, the first machine learning tool used in the UK to fight Covid-19, which allowed clinicians to predict utilization of scarce resources such as ventilators and ICU beds, and entering a partnership with the NHS for real-world use of Cambridge Adjutorium at Acute Trusts in England; 
  • conducted some of the first research and statistical analysis regarding the nature of the disease, its spread, and its disproportionate impact on certain minorities and/or disadvantaged communities world-wide;
  • creating Policy Impact Predictor (PIP), a world’s first machine learning tool developed to guide government decision-making around measures to prevent the spread of COVID-19;
  • exploring and offering the first guidance regarding the potential impact of machine learning on clinical trials during the pandemic.

Cambridge Adjutorium partnership with NHS Digital

During the height of the COVID-19 pandemic, healthcare systems around the world saw an unbelievable amount of pressure on capacity. Ventilators and ICU beds were in short supply, and the time of clinical professionals was stretched across too many patients to cover.

To help hospitals respond, our lab developed Cambridge Adjutorium, a prediction and capacity management tool capable of providing hospital-level projections of upcoming demand for ventilators and ICU beds. We successfully trained Cambridge Adjutorium using depersonalised COVID-19 patient data provided by Public Health England, and reached an agreement with the NHS for real-world use of Cambridge Adjutorium at a number of Acute Trusts in England. As a result, Cambridge Adjutorium (implemented by the NHS under the name “CPAS”) was one of the first machine learning-based systems to be deployed in hospitals on a national scale to address the COVID-19 pandemic.

We were able to develop Cambridge Adjutorium and provide it to the NHS at speed by adapting a very general automated machine learning framework called AutoPrognosis (also developed by our lab). 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.

NHS partnership announcement
CPAS: the UK’s National Machine Learning-based Hospital Capacity Planning System for COVID-19 (Machine Learning, November 2020)

Machine learning for adaptive clinical trials

The COVID-19 pandemic highlighted the many difficulties weighing on the process developing, trialing, and approving vaccines within a short timeframe.

In a paper authored jointly by the van der Schaar Lab’s researchers, alongside colleagues from AstraZeneca, Novartis, and academics within the broader machine learning community, we demonstrated how machine learning methods could substantially assist in the identification, approval and distribution of treatments and vaccines for diseases like COVID-19. The paper, published in Statistics in Biopharmaceutical Research in July 2020, placed particular emphasis on three particular challenges:

  • ongoing clinical trials for non-COVID-19 drugs;
  • clinical trials for repurposing drugs to treat COVID-19; and
  • clinical trials for new drugs to treat COVID-19.
Machine learning for clinical trials in the era of COVID-19 (Statistics in Biopharmaceutical Research, July 2020)

Policy Impact Predictor (PIP)

Policy Impact Predictor (PIP) is a machine learning tool developed to guide government decision-making around measures to prevent the spread of COVID-19.

In addition to accurately modeling COVID-19 mortality trends under current policy sets, PIP can adaptively tailor forecasts to show the potential impact of specific policy changes, such as reopening schools or workplaces, implementing mask mandates, or relaxing shelter-in-place requirements.

PIP is also able to tackle “What if?” policy questions looking into the past. For example, PIP can estimate what would have happened if Italy’s government had waited a week before imposing lockdown measures.

PIP builds on a two-layer machine learning-based compartmental model introduced in a paper by Zhaozhi Qian, Ahmed Alaa, and Mihaela van der Schaar, which was published at NeurIPS 2020.

When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes (NeurIPS 2020)

Research and statistical analysis

Since the start of the COVID-19 pandemic, we have conducted wide-ranging research and statistical analysis aiming to better understand the disease and its impact on specific individuals and groups. This work has spanned multiple continents, involving a diverse group of academic and clinical collaborators and a variety of datasets.

Much of this research has been presented in leading medical journals. Several highlights are presented below.

Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression (NeurIPS 2021)
Retrospective cohort study of admission timing and mortality following COVID-19 infection in England (BMJ Open, November 2020)
Between-centre differences for COVID-19 ICU mortality from early data in England (Intensive Care Medicine, June 2020)
Ethnic and regional variations in hospital mortality from COVID-19 in Brazil: a cross-sectional observational study (The Lancet Global Health, July 2020)
Comparing COVID-19 risk factors in Brazil using machine learning: the importance of socioeconomic, demographic and structural factors (Nature Scientific Reports, August 2021)

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