Clinically-applicable prediction of hospital stay and patient similarity retrieval in paediatric cardiology using machine learning.
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| Title: | Clinically-applicable prediction of hospital stay and patient similarity retrieval in paediatric cardiology using machine learning. |
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| Authors: | Rigny L; Department of Brain Sciences, Imperial College, London, UK. louise.rigny22@imperial.ac.uk.; Innovation and Virtual Environments, NIHR Great Ormond Street Hospital Biomedical Research Centre and GOSH Data Research, London, UK. louise.rigny22@imperial.ac.uk., Biggart I; Department of Brain Sciences, Imperial College, London, UK.; Care Research and Technology Centre, UK Dementia Research Institute, London, UK., Zakka K; Innovation and Virtual Environments, NIHR Great Ormond Street Hospital Biomedical Research Centre and GOSH Data Research, London, UK.; Department of Pediatrics, MedStar Georgetown University Hospital, Washington, DC, USA., Li J; Department of Brain Sciences, Imperial College, London, UK.; Innovation and Virtual Environments, NIHR Great Ormond Street Hospital Biomedical Research Centre and GOSH Data Research, London, UK., Capstick A; Department of Brain Sciences, Imperial College, London, UK.; Care Research and Technology Centre, UK Dementia Research Institute, London, UK., Sabu S; Innovation and Virtual Environments, NIHR Great Ormond Street Hospital Biomedical Research Centre and GOSH Data Research, London, UK., Rajendran P; Innovation and Virtual Environments, NIHR Great Ormond Street Hospital Biomedical Research Centre and GOSH Data Research, London, UK., Sridharan S; Innovation and Virtual Environments, NIHR Great Ormond Street Hospital Biomedical Research Centre and GOSH Data Research, London, UK., Farooq S; Innovation and Virtual Environments, NIHR Great Ormond Street Hospital Biomedical Research Centre and GOSH Data Research, London, UK., Taylor A; Innovation and Virtual Environments, NIHR Great Ormond Street Hospital Biomedical Research Centre and GOSH Data Research, London, UK.; Institute of Cardiovascular Science, University College London, London, UK., Booth J; Innovation and Virtual Environments, NIHR Great Ormond Street Hospital Biomedical Research Centre and GOSH Data Research, London, UK., Patel S; Innovation and Virtual Environments, NIHR Great Ormond Street Hospital Biomedical Research Centre and GOSH Data Research, London, UK., Ramnarayan P; Innovation and Virtual Environments, NIHR Great Ormond Street Hospital Biomedical Research Centre and GOSH Data Research, London, UK.; Department of Surgery and Cancer, Imperial College, London, UK.; Imperial College Healthcare NHS Trust, St Mary's Hospital, London, UK., Sebire N; Innovation and Virtual Environments, NIHR Great Ormond Street Hospital Biomedical Research Centre and GOSH Data Research, London, UK.; Department of Population, Policy and Practice, Institute of Child Health, University College London,, London, UK., Ng C; Innovation and Virtual Environments, NIHR Great Ormond Street Hospital Biomedical Research Centre and GOSH Data Research, London, UK., Barnaghi P; Department of Brain Sciences, Imperial College, London, UK. p.barnaghi@imperial.ac.uk.; Innovation and Virtual Environments, NIHR Great Ormond Street Hospital Biomedical Research Centre and GOSH Data Research, London, UK. p.barnaghi@imperial.ac.uk.; Care Research and Technology Centre, UK Dementia Research Institute, London, UK. p.barnaghi@imperial.ac.uk.; Department of Population, Policy and Practice, Institute of Child Health, University College London,, London, UK. p.barnaghi@imperial.ac.uk. |
| Source: | Nature communications [Nat Commun] 2026 May 13; Vol. 17 (1). Date of Electronic Publication: 2026 May 13. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Nature Pub. Group Country of Publication: England NLM ID: 101528555 Publication Model: Electronic Cited Medium: Internet ISSN: 2041-1723 (Electronic) Linking ISSN: 20411723 NLM ISO Abbreviation: Nat Commun Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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