Outcome Prediction and Decision Support for Stroke using Routine Data
Research type
Research Study
Full title
Outcome Prediction and Decision Support for Stroke using Routine Data
IRAS ID
302301
Contact name
Philip Clatworthy
Contact email
Sponsor organisation
North Bristol NHS Trust
Duration of Study in the UK
2 years, 11 months, 30 days
Research summary
Stroke is a major cause of disability amongst adults worldwide. Treatment decisions depend on clinicians’ ability to anticipate the consequences of different courses of action. In stroke, this frequently involves combining information gained from imaging (both images and radiology reports) with knowledge of an individual patient’s current status and medical history. Decision support tools exist which, for example, analyse imaging data to provide insights, or use basic clinical data, sometimes combined with a very basic imaging parameter e.g. stroke lesion volume, to help clinicians make treatment decisions.
This project will use a combination of clinical and imaging data to predict outcome after stroke and where appropriate estimate the effect of different treatment choices on clinical outcomes. Machine learning methods including deep learning techniques employing convolutional networks, and natural language processing, will be used alongside conventional analysis techniques.
REC name
London - Queen Square Research Ethics Committee
REC reference
22/PR/0122
Date of REC Opinion
22 Sep 2022
REC opinion
Further Information Favourable Opinion