MICA: InterdisciPlInary Collaboration for EfficienT and Effective Use of Clinical Images in Big Data Health Care RESearch: PICTURES (Programme Grant) (Joint with Universities of Edinburgh and Abertay)

Doney, Alexander (Investigator)
Jefferson, Emily (Investigator)
Palmer, Colin (Investigator)
Steele, Douglas (Investigator)
Trucco, Manuel (Investigator)
Wang, Huan (Investigator)

Status	Finished
Effective start/end date	1/08/19 → 28/02/25

Funding

Medical Research Council (MR/S010351/1): £1,731,488.00

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 project contributes towards the following SDG(s):

Health Informatics Centre
Health and Clinical Services
Facility/equipment: Facility

1 Invited talk

Designing machine learning experiments using SLURM within a Cloud Trusted Research Environment
Cole, C. (Contributor), Reel, P. (Speaker), Jackson, A. (Contributor), Krueger, S. (Contributor), Doney, A. (Contributor) & Steele, D. (Contributor)
5 Sept 2023
Activity: Talk or presentation types › Invited talk

11 Article
3 Review article
2 Preprint
1 Chapter
More
- 1 Poster

An end-to-end workflow for routine clinical MRI brain scans and linked prospective electronic medical records to predict future dementia using a Support Vector Machine (SVM) based Machine Learning Approach: A Feasibility Study
Reel, P. S., Al-Wasity, S., Edwards, C., Reel, S., Mansouri-Benssassi, E., Suveges, S., Krueger, S., Jefferson, E., Doney, A. & Steele, D., 25 Feb 2025.
Research output: Contribution to conference › Poster › peer-review

Open Access
File
The Scottish Medical Imaging Archive: 57.3 million Radiology Studies Linked to their Medical Records
Baxter, R., Nind, T., Sutherland, J., McAllister, G., Hardy, D., Hume, A., MacLeod, R., Caldwell, J., Krueger, S., Tramma, L., Teviotdale, R., Gillen, K., Scobie, D., Bailie, I., Brooks, A., Prodan, B., Kerr, W., Sloan-Murphy, D., Herrera, J. F. R. & van Beek, E. J. R. & 12 others, Reel, P. S., Reel, S., Mansouri-Benssassi, E., Mudie, R., Steele, D., Doney, A., Trucco, E., Morris, C., Wallace, R., Morris, A., Parsons, M. & Jefferson, E. (Lead / Corresponding author), Jan 2024, In: Radiology: Artificial Intelligence. 6, 1, 6 p., e220266.
Research output: Contribution to journal › Article › peer-review

Open Access
File
9 Citations (Scopus)

129 Downloads (Pure)
Disclosure control of machine learning models from trusted research environments (TRE): New challenges and opportunities
Mansouri-Benssassi, E., Rogers, S., Reel, S., Malone, M., Smith, J., Ritchie, F. & Jefferson, E. (Lead / Corresponding author), Apr 2023, In: Heliyon. 9, 4, 11 p., e15143.
Research output: Contribution to journal › Review article › peer-review

Open Access
File
2 Citations (Scopus)

112 Downloads (Pure)

MICA: InterdisciPlInary Collaboration for EfficienT and Effective Use of Clinical Images in Big Data Health Care RESearch: PICTURES (Programme Grant) (Joint with Universities of Edinburgh and Abertay)

Project Details

Funding

UN Sustainable Development Goals

Fingerprint

Health Informatics Centre

Designing machine learning experiments using SLURM within a Cloud Trusted Research Environment

An end-to-end workflow for routine clinical MRI brain scans and linked prospective electronic medical records to predict future dementia using a Support Vector Machine (SVM) based Machine Learning Approach: A Feasibility Study

The Scottish Medical Imaging Archive: 57.3 million Radiology Studies Linked to their Medical Records

Disclosure control of machine learning models from trusted research environments (TRE): New challenges and opportunities

MICA: InterdisciPlInary Collaboration for EfficienT and Effective Use of Clinical Images in Big Data Health Care RESearch: PICTURES (Programme Grant) (Joint with Universities of Edinburgh and Abertay)

Project Details

Funding

UN Sustainable Development Goals

Fingerprint

Equipment

Health Informatics Centre

Activities

Designing machine learning experiments using SLURM within a Cloud Trusted Research Environment

Research output

An end-to-end workflow for routine clinical MRI brain scans and linked prospective electronic medical records to predict future dementia using a Support Vector Machine (SVM) based Machine Learning Approach: A Feasibility Study

The Scottish Medical Imaging Archive: 57.3 million Radiology Studies Linked to their Medical Records

Disclosure control of machine learning models from trusted research environments (TRE): New challenges and opportunities