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

doi:10.1016/j.imu.2023.101431

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 journal › Comment/debate › peer-review

Abstract

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 language	English
Article number	101431
Journal	Informatics in Medicine Unlocked
DOIs	https://doi.org/10.1016/j.imu.2023.101431
Publication status	Accepted/In press - 2023

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Alskaf, E., Frey, S. M., Scannell, C. M., Suinesiaputra, A., Vilic, D., Dinu, V., Masci, P. G., Perera, D., Young, A., & Chiribiri, A. (Accepted/In press). 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)). Informatics in Medicine Unlocked, Article 101431. https://doi.org/10.1016/j.imu.2023.101431

Alskaf, Ebraham ; Frey, Simon M. ; Scannell, Cian M. et al. / 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)). In: Informatics in Medicine Unlocked. 2023.

@article{a464e57c65824e06a1a249601d10d32d,

title = "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))",

abstract = "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.",

author = "Ebraham Alskaf and Frey, {Simon M.} and Scannell, {Cian M.} and Avan Suinesiaputra and Dijana Vilic and Vlad Dinu and Masci, {Pier Giorgio} and Divaka Perera and Alistair Young and Amedeo Chiribiri",

note = "Publisher Copyright: {\textcopyright} 2023",

year = "2023",

doi = "10.1016/j.imu.2023.101431",

language = "English",

journal = "Informatics in Medicine Unlocked",

issn = "2352-9148",

publisher = "Elsevier Ltd",

}

Alskaf, E, Frey, SM, Scannell, CM , Suinesiaputra, A, Vilic, D, Dinu, V , Masci, PG , Perera, D , Young, A & Chiribiri, A 2023, '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))', Informatics in Medicine Unlocked. https://doi.org/10.1016/j.imu.2023.101431

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)). / Alskaf, Ebraham; Frey, Simon M.; Scannell, Cian M. et al.
In: Informatics in Medicine Unlocked, 2023.

Research output: Contribution to journal › Comment/debate › peer-review

TY - JOUR

T1 - 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))

AU - Alskaf, Ebraham

AU - Frey, Simon M.

AU - Scannell, Cian M.

AU - Suinesiaputra, Avan

AU - Vilic, Dijana

AU - Dinu, Vlad

AU - Masci, Pier Giorgio

AU - Perera, Divaka

AU - Young, Alistair

AU - Chiribiri, Amedeo

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85180282336&partnerID=8YFLogxK

U2 - 10.1016/j.imu.2023.101431

DO - 10.1016/j.imu.2023.101431

M3 - Comment/debate

AN - SCOPUS:85180282336

SN - 2352-9148

JO - Informatics in Medicine Unlocked

JF - Informatics in Medicine Unlocked

M1 - 101431

ER -

Alskaf E, Frey SM, Scannell CM , Suinesiaputra A, Vilic D, Dinu V et al. 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)). Informatics in Medicine Unlocked. 2023;101431. doi: 10.1016/j.imu.2023.101431

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