Deriving an actionable patient phenome from healthcare data
PI, Funded by MRC, Feb 18 - Feb 21, £315,181
The proposed research will devise a semantic electronic health record toolkit that is able to derive a consistent and comprehensive patient phenome from unstructured and structured electronic health records and provide semantic computation upon it to support decision making for tailored care, trial recruitment and research.
Towards an AI-driven Health Informatics Platform for supporting clinical decision making in Scotland – a pilot study in NHS Lothian
PI, Funded by Wellcome Trust iTPA, Jan - Dec 2020, £19,200
Our long term objective is to enhance Electronic Health Records (EHRs) across NHS Lothian Health Board using artificial intelligence (AI) driven data science infrastructure to benefit patients and the health service provision. This project will serve as a pilot study for the larger Data Loch City Deal collaboration, which aims to use all of our health and social care data assets to drive research and innovation, improve patient care and reduce health inequalities for all patients.
This project will develop two exemplar use cases in NHS Lothian: (a) improving the management of hypoglycaemia; and (b) decision support in prescribing anticoagulants to patients with Atrial Fibrillation.
Graph-Based Data Federation for Healthcare Data Science
CoI, Academic Lead, Funded by MRC, Mar 19 - Nov 19, £260,057
We know that answers to many in-depth healthcare questions can only be explored if we can look across data for the whole UK. However, we manage data locally and describe it in different ways to suit local communities. To get a global view from local data we need a map that tells us precisely where to look for data and how to interpret it when we find it. If we have this sort of map then we can use it to link data between localities in a way that makes access more predictable and rapid while also allowing the people managing different data sets to retain control of how the data in their charge is shared. We can also treat the map itself as data that can be shared to give insights into potential uses of data linkage and to encourage as wide a variety of innovators as possible to build tools that can be used across the data landscape; enriching the data, revealing new knowledge and extending the map.
Scotland Technical Lead, Funded by MRC / HDR UK, Oct 19 - Mar 20, £650,000
We will establish a natural language processing (NLP) processing research community that will address the complexity of clinical text through development of shared tools and standards with inbuilt patient confidence and engagement, supporting joint working across industry, academia and the NHS. The community will be open and inclusive, and develop capability for UK-wide NLP research at scale whilst providing clear ‘quick-wins’ through exemplar projects, shared material and datasets for training and implementation, with the ultimate aim of integrating with other health data analytics. The project will lay the foundations for a sustainable model for collaborative working, thus attracting funding for next 4 years and beyond.