DATA 
SCIENCE
FOR
HEALTH
EQUITY

We were delighted to host Anna Palczewska, Rob Grout, and Frank Wood for the latest instalment of our monthly informal meetup - The DSxHE Download. Title: Predict, Prevent, Perform: Partnerships to Unlock the Power of AI & Analytics The recording is just the presentation part of the session - this was followed by discussion and Q&A which we did not record. In this presentation, we will be joined by colleagues from the Leeds Office of Data Analytics and Accenture who worked i

Here is the recording of our ECR-led webinar on why research on children and families matters  and how inclusive, collaborative, and data-driven approaches can help advance health equity for children, young people, and families . Julia Shumway, UCL About Julia: Julia Shumway is a PhD student and research assistant at the University College London Great Ormond Street Institute of Child Health. The aim of her PhD is to understand the impact of placement in mainstream versus spe

We were delighted to host Carrie Alexander , for the latest instalment of our monthly informal meetup - The DSxHE Download. Title: AI Governance as a Knowledge Game The recording is just the presentation part of the session - this was followed by discussion and Q&A which we did not record. AI Ethics and Governance frameworks have proliferated in recent years, accompanied by criticism from hard law advocates who argue, for instance, that AI ethics frameworks are “useless” bec

We were delighted to host Claire Coffey - University of Cambridge , for the latest instalment of our webinar series on statistical methods for fairness and equity in healthcare. Topic:   Are polygenic risk scores fair for cardiovascular disease risk prediction?  Abstract:  Polygenic risk scores (PRS) are increasingly proposed to enhance cardiovascular disease (CVD) risk prediction, particularly for individuals whose clinical risk estimates fall in intermediate ranges where tr

We were delighted to host  Vincent Jeanselme a postdoctoral researcher at the reAIM Lab, Columbia University, for the latest instalment of our webinar series on statistical methods for fairness and equity in healthcare. Topic:   Advancing AI-Based Risk Prediction for Improved Decision-Making in Healthcare Abstract Accurate prediction of patient outcomes plays a critical role in healthcare decision-making, shaping treatment strategies, clinical guidelines, hospital operations

Recording of the launch of the Data Diversity Theme, a new initiative from Data Science for Health Equity (DSxHE) in partnership with Cancer Research UK. Don't want to miss out on any future Data Diversity theme activities? Stay up to date by joining the #theme-data-diversity channel on our Slack workspace .

We were delighted to host Jake Hightower, data science and global health innovator, for the latest instalment of our webinar series on statistical methods for fairness and equity in healthcare. Topic:   Preserving Health Equity in a Global Pandemic Model Abstract  In an era of increasingly complex global health threats, pandemic modelling must evolve to reflect not only the spread of disease but the lived realities of those most affected. This talk presents a ground-breaking

We recently hosted a couple of virtual drop-in sessions to launch our latest DSxHE Theme on Data Diversity . These sessions served as both an introduction to the new theme and a starting point for an important conversation about diversity in biomedical datasets. The event featured interactive activities and thought-provoking discussions designed to spark engagement and reflection ( check out this blog post for more on that ). We also recorded a special segment with Brieuc Leh

We were delighted to host Stephen Pfohl, Senior Research Scientist at Google Research, for the latest instalment of our webinar series on statistical methods for fairness and equity in healthcare. Stephen shared insights from his research into the challenges of evaluating algorithmic fairness in clinical settings, highlighting that fairness assessments are never one-size-fits-all. Drawing on examples from his work, Stephen emphasized that evaluations of fairness must be groun

We were pleased to welcome Dr. John Ford, Director of the Health Equity Evidence Centre, for a recent webinar on using machine learning to tackle health and care inequalities. Dr. Ford shared how his team, in collaboration with the EPPI Centre at UCL, is developing living evidence maps , continuously updated tools that organise research on effective interventions. These maps address a growing challenge in public health: keeping pace with an ever-expanding evidence base, where

We were thrilled to host Dr. Briana Stephenson from the Harvard T.H. Chan School of Public Health in a past installment of our webinar series! Dr. Stephenson led a fascinating discussion on how mixture models can help uncover important patterns within underserved populations. She highlighted how traditional research methods often focus on majority subgroups, leaving smaller communities overlooked. In a diverse country like the United States, this "one-size-fits-all" approach

Last month we welcomed Charles Jones to present his latest work at the Statistical Methods for Health Equity webinar series. Charles Jones is a fourth-year PhD student at Imperial College London, advised by Professor Ben Glocker. Charles' research is at the intersection of fairness and causality in machine learning for medical imaging. He is interested in how causality may be used as a unifying language to understand (and begin to solve) core problems in machine learning, suc

We’re excited to share the recording of DSxHE Mental Health Webinar  with Ciarán O'Driscoll , Associate Professor in Clinical Psychology at UCL, and Programme Director of the Clinical Associate in Psychology course. Ciarán’s work bridges the gap between clinical psychology and data science, and in this talk, he explores how data analytics  is transforming secondary care mental health services . Ciarán has extensive experience teaching mental health professionals and working w

We’re excited to share our DSxHE Mental Health Webinar  with Kim de Jong , Assistant Professor in Clinical Psychology at Leiden University , now live on YouTube! In this insightful talk, Kim dives into how routine outcome monitoring  and feedback  can help mental health professionals deliver better, data-informed care. Kim’s research focuses on personalizing treatment for individuals, examining why some patients respond better to certain therapies than others, and exploring h

In our latest DSxHE Mental Health Webinar, we had the honor of hosting Ellen Driessen, Assistant Professor in Clinical Psychology at Radboud University in the Netherlands. Ellen, a renowned researcher with numerous awards, including from the Dutch Government, shared her groundbreaking work on personalized treatment approaches for depression. As a clinical psychologist, Ellen’s research is centered on improving treatment for depression by focusing not just on the efficacy of t

This month we welcomed Dr. Nathan Blake to present his work at the Statistical Methods for Health Equity webinar series. Nathan has 12 years of experience as a nurse, predominantly in A&E, before completing a PhD in computational biology. His current work looks at applying causal reasoning for explaining the outputs of black-box AI models as applied to clinical problems. In this talk, Nathan explains the need for Explainable Artificial Intelligence (XAI) in the clinical setti

Catch up on the talk from Irving Amukasa for the Statistical Methods for Health Equity webinar series. Irving Amukasa is a Tech Entrepreneur, self-taught Android and AI developer, founder and CEO of Sophie Bot. Irving is a co-organizer of Android 254, an android developer group based in Kenya and previous organizer of droidcon.co.ke , the largest android focus developer conference in Africa. Irving is also a Windows Insider 4 Good fellow and MentorNations ambassador. Irving g

Catch up on the excellent talk from Dr Divya Shanmugam for the Statistical Methods for Health Equity webinar series. Dr Divya Shanmugam, recently completed her PhD at the Massachusetts Institute of Technology, working with Prof. John Guttag on best practices for developing and deploying machine learning models in the presence of unreliable data. Divya shared her fantastic work on how to characterize and mitigate the impact of imperfect data on machine learning models deployed