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Paper on COVID-19 hospital capacity planning published in Machine Learning

Zhaozhi QianAhmed AlaaMihaela van der SchaarNick Maxfield
September 26, 2020
2 min read

A paper authored by Zhaozhi Qian, Ahmed Alaa, and Mihaela van der Schaar has been accepted for publication in Machine Learning (editor-in-chief: Hendrik Blockeel).

The paper, entitled “CPAS: the UK’s National Machine Learning-based Hospital Capacity Planning System for COVID-19,” was accepted for publication on September 24.

The authors introduce a capacity planning and analysis system (CPAS) developed to enable clinicians to predict utilization of scarce resources, such as ventilators and ICU beds, during the ongoing COVID-19 pandemic. The system is the result of a partnership (announced in April) between the van der Schaar Lab, NHS Digital, and Public Health England, and was built around Cambridge Adjutorium, a prediction system created by the van der Schaar Lab.

CPAS is one of the first machine learning-based systems to be deployed in hospitals on a national scale to address the COVID-19 pandemic.

CPAS: the UK’s National Machine Learning-based Hospital Capacity Planning System for COVID-19

Zhaozhi Qian, Ahmed Alaa, Mihaela van der Schaar

The coronavirus disease 2019 (COVID-19) global pandemic poses the threat of overwhelming healthcare systems with unprecedented demands for intensive care resources. Managing these demands cannot be effectively conducted without a nationwide collective effort that relies on data to forecast hospital demands on the national, regional, hospital and individual levels.

To this end, we developed the COVID-19 Capacity Planning and Analysis System (CPAS) — a machine learning-based system for hospital resource planning that we have successfully deployed at individual hospitals and across regions in the UK in coordination with NHS Digital.

In this paper, we discuss the main challenges of deploying a machine learning-based decision support system at national scale, and explain how CPAS addresses these challenges by (1) defining the appropriate learning problem, (2) combining bottom-up and top-down analytical approaches, (3) using state-of-theart machine learning algorithms, (4) integrating heterogeneous data sources, and (5) presenting the result with an interactive and transparent interface.

CPAS is one of the first machine learning-based systems to be deployed in hospitals on a national scale to address the COVID-19 pandemic — we conclude the paper with a summary of the lessons learned from this experience.

For a full list of the van der Schaar Lab’s publications, click here.

To find out more about the van der Schaar Lab’s work related to the COVID-19 pandemic, visit our dedicated page here.

Zhaozhi Qian

After obtaining a MSc in Machine Learning at UCL, Zhaozhi Qian started his career as a data scientist in the largest mobile gaming company in Europe. Three years later, he found it might be more fulfilling to apply AI to cure cancer than to make the gamers hit the purchase button 1% more often.

He thus joined the group in 2019 as a PhD student focusing on robust and interpretable learning for longitudinal data. So far, his work has included inferring latent disease interaction networks from Electronic Health Records, uncovering the causal structure between events that unfold over time, and calibrating the predictive uncertainty under domain shift.

Zhaozhi also worked as a contractor in the NHS during the COVID-19 pandemic contributing his analytical skills to the national response to the pandemic.

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Ahmed Alaa

Ahmed M. Alaa is a Postdoctoral Scholar at the ECE Department, University of California, Los Angeles (UCLA), and an affiliated Postdoctoral Researcher at the Department of Applied Mathematics and Theoretical Physics, University of Cambridge.

His primary research focus has been on causal inference, automated machine learning, uncertainty quantification and time-series analysis.

He has published papers in several leading machine learning conferences including NeurIPS, ICML, ICLR and AISTATS.

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Mihaela van der Schaar

Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge and a Fellow at The Alan Turing Institute in London.

Mihaela has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award.

In 2019, she was identified by National Endowment for Science, Technology and the Arts as the most-cited female AI researcher in the UK. She was also elected as a 2019 “Star in Computer Networking and Communications” by N²Women. Her research expertise span signal and image processing, communication networks, network science, multimedia, game theory, distributed systems, machine learning and AI.

Mihaela’s research focus is on machine learning, AI and operations research for healthcare and medicine.

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Nick Maxfield

From 2020 to 2022, Nick oversaw the van der Schaar Lab’s communications, including media relations, content creation, and maintenance of the lab’s online presence.

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