Matthew Sperrin

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PhD projects

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Personal profile

Overview

I am internationally leading in methods and applications in the fields of clinical prediction modelling and notably, its intersection with causal inference. I have extensive experience in teaching and learning, and am Programme Director of the MSc Health Data Science. 

Research interests

My approach is to start with genuine clinical problems where additional decision support (through prediction models) is warranted, particularly cases where standard methods don’t suffice. Because of this problem-first approach my work is implemented in healthcare systems. My focuses are:

  1. The role of causal inference in prediction, to enable prediction under intervention. This is directly beneficial to decision makers - standard prediction models consider correlation only, so do not allow ‘what-if’ (prediction under intervention) questions like ‘what if I stopped smoking’ to be answered - but these are exactly the questions decision-makers have. I am developing methods to answer exactly these questions - e.g., through the Causality in Healthcare AI (CHAI) hub UKRI funding, but also following this through to direct application - e.g., through the CHARIOT project where we are building a prediction under intervention model for cardiovascular disease prevention. I also study the role of causal inference in improving generalisability and transportability of models, and to elucidate fairness and bias issues.
  2. Dealing with missing data when making predictions, and the inverse problem of adaptive observation - determining what should be measured, and when, at an individual level.
  3. Ensuring models remain up-to-date across space and time using dynamic modelling and updating.

These three broad areas are motivated by improving the pathway to implementation for prediction models in healthcare. Clinical application areas include primary prevention of long term conditions (especially CVD), and treatment in cancer. I work mostly with routinely collected electronic health record data.

Teaching

I have wide ranging experience of course leadership and curriculum development in the undergraduate, postgraduate, and CPD contexts, having developed and delivered courses across all three. I have taught in two universities, both to ‘specialist’ students (e.g. mathematicians and MSc students) and to ‘service’ students (e.g. skills courses for the NHS). I use a range of innovative teaching approaches, including blended learning.

I am Programme Director for the MSc Health Data Science.

Memberships of committees and professional bodies

Royal Statistical Society, CStat.

SFHEA

Methodological knowledge

Statistics, Epidemiology, Data Science

Qualifications

PhD, Statistics, Lancaster University (2010)

MMORSE (Masters in Maths, Operational Research, Statistics and Economics), University of Warwick (2006)

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

  • SDG 3 - Good Health and Well-being
  • SDG 4 - Quality Education
  • SDG 5 - Gender Equality
  • SDG 10 - Reduced Inequalities

Research Beacons, Institutes and Platforms

  • Institute for Data Science and AI
  • Digital Futures
  • Christabel Pankhurst Institute
  • Healthier Futures
  • Manchester Cancer Research Centre
  • Cancer
  • Global inequalities

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Collaborations and top research areas from the last five years

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