Machine learning outcome prediction using stress perfusion cardiac magnetic resonance reports and natural language processing of electronic health records

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.2023.101418

Machine learning outcome prediction using stress perfusion cardiac magnetic resonance reports and natural language processing of electronic health records

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

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Original language	English
Article number	101418
Number of pages	9
Journal	Informatics in Medicine Unlocked
Volume	44
DOIs	https://doi.org/10.1016/j.imu.2023.101418
Publication status	Published - Jan 2024

Funding

After fitting and training machine learning models on clinical variables to predict mortality, support vector machine (SVM) performed best [F1 score = 0.24, AUC = 0.80].The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Dr Ebraham Alskaf reports financial support was provided by Siemens Healthineers. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Funders	Funder number
Siemens Healthineers

Keywords

Cardiac magnetic resonance
Coronary artery disease
Electronic health records
Machine learning
Natural language processing
Outcome prediction

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10.1016/j.imu.2023.101418

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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. (2024). Machine learning outcome prediction using stress perfusion cardiac magnetic resonance reports and natural language processing of electronic health records. Informatics in Medicine Unlocked, 44, Article 101418. https://doi.org/10.1016/j.imu.2023.101418

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title = "Machine learning outcome prediction using stress perfusion cardiac magnetic resonance reports and natural language processing of electronic health records",

keywords = "Cardiac magnetic resonance, Coronary artery disease, Electronic health records, Machine learning, Natural language processing, Outcome prediction",

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",

year = "2024",

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doi = "10.1016/j.imu.2023.101418",

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volume = "44",

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Alskaf, E, Frey, SM, Scannell, CM, Suinesiaputra, A, Vilic, D, Dinu, V, Masci, PG, Perera, D, Young, A & Chiribiri, A 2024, 'Machine learning outcome prediction using stress perfusion cardiac magnetic resonance reports and natural language processing of electronic health records', Informatics in Medicine Unlocked, vol. 44, 101418. https://doi.org/10.1016/j.imu.2023.101418

Machine learning outcome prediction using stress perfusion cardiac magnetic resonance reports and natural language processing of electronic health records. / Alskaf, Ebraham (Corresponding author); Frey, Simon M.; Scannell, Cian M. et al.
In: Informatics in Medicine Unlocked, Vol. 44, 101418, 01.2024.

Research output: Contribution to journal › Article › Academic › peer-review

TY - JOUR

T1 - Machine learning outcome prediction using stress perfusion cardiac magnetic resonance reports and natural language processing of electronic health records

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 - 2024/1

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KW - Coronary artery disease

KW - Electronic health records

KW - Machine learning

KW - Natural language processing

KW - Outcome prediction

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DO - 10.1016/j.imu.2023.101418

M3 - Article

C2 - 38173908

AN - SCOPUS:85179097046

SN - 2352-9148

VL - 44

JO - Informatics in Medicine Unlocked

JF - Informatics in Medicine Unlocked

M1 - 101418

ER -

Machine learning outcome prediction using stress perfusion cardiac magnetic resonance reports and natural language processing of electronic health records

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