@article{46b2e63a2fc6404db938a97bb5046e1d,
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",
note = "Funding Information: 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.",
year = "2024",
month = jan,
doi = "10.1016/j.imu.2023.101418",
language = "English",
volume = "44",
journal = "Informatics in Medicine Unlocked",
issn = "2352-9148",
publisher = "Elsevier Ltd",
}