Research in HIC

HIC supports research both as a service and as a collaboration

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The HIC Research Team is led by Dr Christian Cole, Senior Lecturer in Health Informatics in the School of Medicine.

The service side is mostly supported via data and/or software services where the research team can advise on how to approach or develop a research question using the data or systems available within HIC. To access the HIC research service please contact HIC Support in the first instance describing your request and we will respond via the most appropriate team.

Additionally, HIC research works closely with the research community collaboratively on either HIC-led projects or projects with HIC as a collaborative partner. HIC is a member of the Health Data Research (HDR) UK Alliance and is involved in several UK-wide initiatives around phenomics, multi-omics and multimorbidity. We have created a next generation Trusted Research Environment (TRE) built upon cloud technology which enables advanced and scalable research on large and complex data.

“Over the last 5 years, we have led or collaborated on research grants totalling £180m”

Health Informatics in the School of Medicine

Contact Chris directly regarding collaborating with us on the following or related research topics:

  • Trusted Research Environment infrastructure
  • FAIR data, data security and data federation
  • Reproducible research
  • Open science
  • AI/ML of patient data records
  • Federated analytics
  • Imaging data
  • Phenotypes

Below we detail examples of current and previous research projects that HIC has either led or collaborated on.

HIC-Led Research

PICTURES: is a £3.8M MRC-funded project led by Dundee in conjunction with the University of Edinburgh, Abertay University and NHS Scotland. The programme is making use of the approximately 30 million clinical images (e.g. MRI, CT) collected since 2006 to develop Artificial Intelligence (AI) methods to search for such ‘warning signs’ in the images which predict the development of diseases. This will allow doctors in the future to make use of this information in routine care, greatly enhancing the clinical utility of routine scans. See the architecture of the PICTURES platform.

 

CO-CONNECT: The COVID -Curated and Open aNalysis aNd rEsearCh plaTform (otherwise known as CO-CONNECT) is a £4M MRC-NIHR funded UK wide research programme supporting researchers to find and access COVID-19 data at pace whilst ensuring people’s information is kept private and secure. CO-CONNECT has embedded a cohort discovery tool within the HDR Innovation Gateway and worked with >20 different organisations who manage data to connect to the tool. Researchers can find out about what data is available but without the data leaving the control of the organisation who manages it.

GRAIMATTER: TREs have historically supported only traditional statistical data analysis. There is an increasing need to also facilitate the training of artificial intelligence (AI) models. The size and complexity of AI models presents significant challenges for the output checking process. Models may be susceptible to external hacking: complicated methods to reverse engineer the learning process to find out about the data used for training, with more potential to lead to re-identification than conventional statistical methods. With input from public representatives, GRAIMatter has assessed a range of tools and methods to support TREs to assess output from AI methods for potentially identifiable information. A Green Paper has been prepared to report on the project’s findings. See a version for a general audience.

TREEHOOSE: has built on HIC’s experience of migrating a TRE hosting anonymised patient data over to ‘public cloud’ – meaning the data is accessible over a secure internet connection rather than in a local data centre – for the benefit of other operators. The project has included a new capability of ‘enclave computing’ to TREs, which will add a layer of software encryption to protect intellectual property and code in addition to the data itself. It released an open-source software to streamline building and operating TREs on public cloud infrastructure whilst maintaining security and trust. Secure enclaves go beyond the traditional TRE infrastructure by adding additional barriers to prevent software algorithms from leaking data.

Phenomics Portal: the National Phenomics Resource project has developed the Phenomics Portal. This reference catalogue of human diseases is integrated into the Health Data Research Innovation Gateway enabling users to search for datasets which may contain their phenotype of interest. A phenotype is an observable and measurable piece of information that is relevant to health or healthcare. For example, it can be a disease (e.g. type 2 diabetes), a blood pressure measurement, a blood sugar value or a prescription of antibiotics. Phenotyping algorithms are special tools that enable researchers to extract phenotypes from complex, and often messy data that get generated during routine interactions within the healthcare system. They identify and extract data from medical records using clinical codes which are the building block of how information is recorded in healthcare (for example ICD-10). Using these specialized algorithms, researchers and clinicians can maximize the value of patient data contained in medical records and answer important questions that can improve health and healthcare.

Alleviate: Alleviate is the Advanced Pain Discovery Platform (APDP) Data Hub. The hub is transforming UK pain datasets to be Findable, Accessible, Interoperable and Reusable (FAIR) and is providing expert data engineering, to enhance responsible, timely and trustworthy analysis by researchers and innovators, with the aim to improve lives. The hub is one of the Health Data Research UK data hubs. 

HIC Collaborations

ENSAT-HT: was an EU-funded Horizon 2020 research and innovation project that to investigate specific types of hypertension and whether it was possible to define specific profiles based on systems biology "-omics" approaches and subsequently use these to define more personalised approaches to disease management. A machine learning model was developed to differentiate between the three different types of Endocrine Hypertension: primary aldosteronism (PA), pheochromocytoma/functional paraganglioma (PPGL) and Cushing’s syndrome (CS).

P4Me: The P4Me initiative is a partnership between Precision Medicine and Pharmacotherapeutics Group at the University of Dundee and NHS Tayside. P4Me is developing the know-how for translation of Precision Medicine research into real world clinical implementation based on Prediction, Prevention, Personalisation, and Participation (P4). The programme incorporates the full spectrum of translational healthcare data science from genomics and imaging-derived biomarkers to remote eHealth solutions. A fundamental component of P4Me is the concept of self-learning healthcare systems whereby evidence of clinical utility is generated as a by-product of clinical activity using novel pragmatic trial designs and natural experiments.

Understanding the Causes of Diseases: This Health Data Research UK project aimed to understand disease at a deeper than ever biological level, to enable us to better predict the onset and progression of ill-health and tailor medicines for sub-types of disease (instead of a one-size-fits-all approach), as well as predict patients’ reaction to medicines. HIC researched how to enhance Trusted Research Environments (TREs) to support new multiomic data types including genomic and imaging data and also to provide High Performance Computing and GPU capabilities within the security controls of the TRE.

The Scottish Federated Safe Havens Project: This Health Data Research UK project researched different federated technological solutions to support the network to streamline processes and new capabilities for data discovery and access across the network. To learn about the network you may wish to read our recent paper. This work continuing with support from Research Data Scotland.

The Centre for Antimicrobial Resistance: The Centre for Antimicrobial Resistance at the University of Dundee brought together biologists, chemists, physicists, clinicians, mathematicians, epidemiologists, engineers and designers to focus on innovation in tackling antimicrobial resistance. For this project, HIC researched how to enhance Trusted Research Environments (TREs) to support new data types including analysis of bacterial genomic data and also to provide High Performance Computing capabilities within the security controls of the TRE.

Multimorbidity and clinical guidelines project: HIC researched the requirements for clinical guideline developers to understand the applicability of trial evidence to inform guideline development. A software tools was developed to enable users to compare the different characteristics of trial eligible and ineligible populations based upon real-world data.

The National COVID-19 Chest Imaging Database (NCCID) Project: NCCID is a centralised UK database containing chest X-ray (CXR), magnetic resonance imaging (MRI) and computed tomography (CT) images from hospital patients across the country. The database was created to support a better understanding of the COVID-19 virus and develop technology which will enable the best care for patients hospitalised with a severe infection. HIC have researched and developed a platform to extract the images from Scotland to include within this database.

The Cambridge Mathematics of Healthcare Hub: The Cambridge Mathematics of Information in Healthcare Hub (CMIH) is a collaboration between mathematics, statistics, computer science and medicine, aiming to develop robust and clinically practical data analytics algorithms for healthcare decision making. The work focusses on some of the most challenging public health problems; Cancer, Cardiovascular Disease, and Dementia. For this project HIC has researched and developed a prototype tool to embed a trained AI model within clinical systems for clinical validation.

The Scottish Improvement Science Collaborating Centre (SISCC): SISCC was a SISCC is a research collaboration that aims to bridge the gap between academia, public, practitioners and policy-makers to enhance quality, safety and person-centred care. In collaboration with ISD and other academic groups, HIC were involved in researching and developing prototype dashboards to help provide General Practitioners with the information they needed to monitor care and prescribing across all their patients.

Data standardisation using the OMOP common data model: This project researched how to convert data into the OMOP model for inclusion in the EHDEN project.

INSPIRED: The INSPIRED project was a 4-year partnership between two institutions, the University of Dundee’s Global Health Research Unit on Global Diabetes Outcomes Research and Madras Diabetes Research Foundation, between them command access to two of the most advanced diabetes management systems in the world. The project focused on building an extensive infrastructure to deliver precision medicine to improve outcomes in patients with diabetes in India. HIC researched TRE enhancements to support genomic data analysis at scale within such environments.

Graph Based Data Federation for Healthcare Data Science: This Health Data Research UK sprint project researched different methods for mapping ontologies including graphical representations.

Application of AI to Heart Failure: In a collaboration between the School of Medicine, UMC Groeningen, National Heart Centre Singapore, US2.ai and Roche we identified data-driven ways to improve the diagnosis of heart failure from echocardiograms.

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Enquiries

Contact Dr Christian Cole directly regarding collaborating with us