Digital Health Case Studies

Find out more about how the N8 CIR has helped Digital Health researchers in furthering their work.

Julie Wilson

Digital Health - University of York

Dr Julie Wilson is a professor in applied statistics in the Department of Mathematics at the University of York. She develops methods for data pre-processing and analysis and has experience in statistical pattern recognition, classification and machine learning techniques.

RSEs were able to help her research by rewriting code (originally in C) in R, which significantly reduced computation time.

  Cell Morphology.pdf

Elizabeth Dickinson

Digital Health - University of York and Croda Europe Ltd

Dr Elizabeth Dickinson is a Post-Doctoral Knowledge Transfer Partnership Associate at the Department of Mathematics, University of York and Croda Europe Ltd. She works on multivariate statistics primarily applied to data from analytical chemistry techniques (chemometrics) and machine learning.
She found working with an RSE helpful as they were able to fix bugs, optimise code, and annotate changes.

  Croda Shiny.pdf

Chris Jewell

Digital Health - Lancaster University

Dr Chris Jewell is a computational epidemiologist. Much of his work is based on using epidemic models to predict the spread of infectious diseases amongst human, animal or plant populations. His current project, GEM: Generalised Epidemic Modelling, is intended to bridge a skills gap between epidemiology, statistics and computationally intensive statistics.

It will make it easier for epidemiologists to access cutting edge statistical tools for modelling epidemics during new disease outbreaks. Methods developers will also benefit as it will be easier and quicker to integrate new statistical innovations and approaches into the investigation of those outbreaks.

  GEM: Generalised Epidemic Modelling

Helena Tendedez

Digital Health - Lancaster University

Respire is a data dashboard that brings together data about patients who have about Chronic Obstructive Pulmonary Disorder from two collaborating NHS trusts. The information is presented to clinicians in a way that ensures that care decisions are based on reliable data, saving time and improving patient care.

In this project RSEs helped to develop visual prototypes of the app to help increase user engagement with the project.

  Respire Dashboard for COPD

Richard Williams

Digital Health - University of Manchester is a web app that enables researchers to construct sets of these clinical codes for their research. Once created they can be saved, validated and reused by other researchers.

  GetSet - Clinical Codes for Research

Peter-John Mäntylä Noble

Digital Health - University of Liverpool

SAVSNET collects clinical notes and questionnaire data from UK veterinary practices alongside results from the UK’s larger veterinary laboratories.

This data is analysed to help monitor disease trends, identify at risk animal populations, provide data to academics, improve public awareness of small animal diseases and provide a route to clinical benchmarking.

In this project RSEs help to manage the software and server architecture required for data collection and added a data dashboard to make it easier to analyse the results.

  Small Animal Veterinary Surveillance Network (SAVSNET)

Fiona Menger, Julie Morris, Matt Forshaw and Becky Osselton

Digital Health - Newcastle University

DAAWN, Digitised Assessment for Aphasia of Written Naming, is a web-based application used to gather information about the process of writing in patients with aphasia, communication challenges in the wake of a stroke or brain injury.

This information is then used to support clinicians speech and language therapy research.

  DAAWN - Digitised Assessment for Aphasia of Written Naming

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