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Artificial Intelligence for Multiple Long-Term Conditions (AIM) Programme - Research Collaboration Stage 2 minutes

 

Contents

Minutes of the Artificial Intelligence for Multiple Long-Term Conditions (AIM) programme committee meeting to discuss Stage 2 applications for Research Collaborations.

Date and location

Meeting held on Monday 22 March 2021 via Zoom.

In Attendance

Committee Members

Professor Lucy Chappell, King's College London (Chair)
Dr Allison Gardner, Keele University
Professor David Hogg, University of Leeds
Dr Frank Ratcliff, Wessex AHSN
Professor Derrick Bennett, University of Oxford
Professor Tim Frayling, University of Exeter
Professor Jose Valderas, University of Exeter
Professor David Leon, London School of Hygiene and Tropical Medicine
Dr Alison Allam, Public Member
Mr Stephen Edgar, Public Member

Department of Health and Social Care

Dr Natalie Owen, Science, Research and Evidence Directorate
Ms Leanne Dew, Science, Research and Evidence Directorate

NIHR Central Commissioning Facility

Mario Moroso, Assistant Director, Research Programmes
Jemma Venables, Senior Research Manager, AIM Programme
Anastasiya Kichigina, Senior Research Manager, Programme Grants
Delanie Dennie, Research Officer, Operations
Ian Beason, Operations Manager
Lara Oh, Research Officer, Operations

Observers

Professor Jorg Huber (Research Design Service)
Dr Louise Hayes (Research Design Service)
Dr Jane Fearnside (Research Design Service)
Dr Sam Norton (Research Design Service)

Research Collaboration Stage 2 applications considered

NIHR202639: Artificial Intelligence and Multimorbidity: Clustering in Individuals, Space and Clinical Context (AIM-CISC)
Conflicts: Professor Jose Valderas
Outcome: Recommended for funding

NIHR202636: DynAIRx: AI for Dynamic prescribing optimisation and care integration in multimorbidity
Conflicts: Professor David Hogg
Outcome: Recommended for resubmission in Wave 2

NIHR202628: Applying Artificial Intelligence to large population data sets to gain new insights into multimorbidity and generate actionable findings to improve the health of the population
Conflicts: None
Outcome: Rejected