Model summary.

<div><p>At the emergency department, it is important to quickly and accurately identify patients at risk of acute myocardial infarction (AMI). One of the main tools for detecting AMI is the electrocardiogram (ECG), which can be difficult to interpret manually. There is a long history of...

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Bibliographic Details
Main Author:	Axel Nyström (15220943) (author)
Other Authors:	Anders Björkelund (15220940) (author), Mattias Ohlsson (334908) (author), Jonas Björk (498175) (author), Ulf Ekelund (115075) (author), Jakob Lundager Forberg (10002543) (author)
Published:	2025
Subjects:	Genetics Cancer Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified moderately sized dataset correctly labeled high art resnet architectures acute myocardial infarction 840 &# 8201 quite data hungry mitigating data requirements large improvements also accurately identify patients transfer learning leads transfer learning data sizes pain patients major improvements xlink "> train models successful strategy straightforward application simple form resulting models predicting ami main tools long history interpret manually emergency department ecg ), detecting ami considerable improvement classify sex ami ). age using 85 auc 79 auc
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