van der Schaar Lab

“Pioneering” COVID-19 study published in Lancet Global Health

A paper authored jointly by the van der Schaar Lab, the University of Cambridge’s Department of Medicine, and a group of Brazilian researchers has been published in The Lancet Global Health (editor-in-chief: Zoë Mullan).

The paper, which was first published on May 27, examines ethnicity as a COVID-19 risk factor in Brazil. One of its key findings is that patients who self-identify as belonging to the country’s pardo and preto ethnicities exhibit significantly higher in-hospital mortality risk than those who self-identify as branco (“white”).

A Lancet Global Health comment calls the work “a pioneering study,” noting that it “confirms in Brazil findings observed in other countries hit hard by COVID-19: that mortality rates from the pandemic differ by geographical region and ethnicity, with disproportionate impact for Black populations and other ethnic minorities.”

View the paper in The Lancet Global Health

Ethnic and regional variations in hospital mortality from COVID-19 in Brazil: a cross-sectional observational study

Pedro Otavio Baqui, Ioana Bica, Valerio Marra, Ari Ercole, Mihaela Van Der Schaar

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.

Ioana Bica

Ioana Bica

Ioana Bica is a second year PhD student at the University of Oxford and at the Alan Turing Institute. She has previously completed a BA and MPhil in Computer Science at the University of Cambridge where she has specialised in machine learning and its applications to biomedicine.

Ioana’s PhD research focuses on building machine learning methods for causal inference and individualised treatment effect estimation from observational data. In particular, she has developed methods capable of estimating the heterogeneous effects of time-dependent treatments, thus enabling us to determine when to give treatments to patients and how to select among multiple treatments over time.

Recently, Ioana has started working on methods for understanding and modelling clinical decision making through causality, inverse reinforcement learning and imitation learning.

Mihaela van der Schaar

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, a Fellow at The Alan Turing Institute in London, and a Chancellor’s Professor at UCLA.

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.

Nick Maxfield

Nick Maxfield

Nick oversees the van der Schaar Lab’s communications, including media relations, content creation, and maintenance of the lab’s online presence.

Nick studied Japanese (BA Hons.) at the University of Oxford, graduating in 2012. Nick previously worked in HQ communications roles at Toyota (2013-2016) and Nissan (2016-2020).

Given his humanities/languages background and experience in communications, Nick is well-positioned to highlight and explain the real-world impact of research that can often be quite esoteric. Thankfully, he is comfortable asking almost endless questions in order to understand a topic.