Corrigendum to “Machine learning outcome prediction using stress perfusion cardiac magnetic resonance reports and natural language processing of electronic health records” [Inform. Med. Unlocked (2024) 1–7/101418] (Informatics in Medicine Unlocked (2024) 44, (S2352914823002642), (10.1016/j.imu.2023.101418))

Ebraham Alskaf*, Simon M. Frey, Cian M. Scannell, Avan Suinesiaputra, Dijana Vilic, Vlad Dinu, Pier Giorgio Masci, Divaka Perera, Alistair Young, Amedeo Chiribiri

*Corresponding author for this work

Research output: Contribution to journalComment/debatepeer-review


The authors regret < Acknowledgement: The authors acknowledge financial support from the Department of Health through the National Institute for Health Research (NIHR) comprehensive Biomedical Research Centre award to Guy's & St Thomas' NHS Foundation Trust in partnership with King's College London and King's College Hospital NHS Foundation Trust and by the NIHR MedTech Co-operative for Cardiovascular Disease at Guy's and St Thomas' NHS Foundation Trust. The work was also supported by Wellcome/EPSRC Centre for Medical Engineering [WT 203148/Z/16/Z] and the Wellcome Trust Innovator Award [222678/Z/21/Z]. The views expressed are those of the authors and not necessarily those of the BHF, the DoH, the EPSRC, the NHS, the NIHR, or the Wellcome Trust.>. The authors would like to apologise for any inconvenience caused.

Original languageEnglish
Article number101431
JournalInformatics in Medicine Unlocked
Publication statusAccepted/In press - 2023

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