Validation of the myocardial-ischaemic-injury-index machine learning algorithm to guide the diagnosis of myocardial infarction in a heterogenous population: a prespecified exploratory analysis

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Title

Validation of the myocardial-ischaemic-injury-index machine learning algorithm to guide the diagnosis of myocardial infarction in a heterogenous population: a prespecified exploratory analysis

Content partner

University of Otago

Collection

Otago University Research Archive

Description

Background Diagnostic pathways for myocardial infarction rely on fixed troponin thresholds, which do not recognise that troponin varies by age, sex, and time within individuals. To overcome this limitation, we recently introduced a machine learning algorithm that predicts the likelihood of myocardial infarction. Our aim was to evaluate whether this algorithm performs well in routine clinical practice and predicts subsequent events. Methods The myocardial-ischaemic-injury-index (MI3) algorithm...

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Format

Research paper

Research format

Scholarly text / Journal article

Date created

2022-05-01

Creator

Doudesis, Dimitrios / Lee, Kuan Ken / Yang, Jason / Wereski, Ryan / Shah, Anoop S. / Tsanas, Athanasios / Anand, Atul / Pickering, John W. / Than, Martin P. / Mills, Nicholas L. / High-STEACS Investigators

URL

https://hdl.handle.net/10523/21941

Related subjects

General & Internal Medicine / Life Sciences & Biomedicine / Medical Informatics / Medicine, General & Internal / Science & Technology

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