Machine learning has shown promising results for risk stratification in patients with ACS. For example, the machine learning-based GRACE version 3.0 score11 improved the prediction of in-hospital mortality among patients with non-ST-elevation acute coronary syndrome (NSTE-ACS) compared with the GRACE version 2.0 score.
Sep 26, 2024
Nov 7, 2013 · Data mining using machine learning techniques may aid in the development of prediction models for Acute Coronary Syndrome (ACS) patients.
Data mining using machine learning techniques may aid in the development of prediction models for Acute Coronary Syndrome (ACS) patients. ACS prediction ...
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Aug 31, 2023 · Machine learning (ML) methods can accurately identify risk factors and predict adverse events. Methods. A total of 5240 patients diagnosed with ...
These types of models have performed with varying degrees of success (Table 1). A catalog of the tools used to risk stratify patients with potential ACS, and ...
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Aug 7, 2020 · Here we report machine learning-based methods for the prediction of underlying acute myocardial ischemia in patients with chest pain.
Oct 20, 2023 · Our meta-analysis aimed to evaluate the predictive value of various ML models in predicting death in ACS patients at different times.
Jan 25, 2023 · We propose a ML-based soft-voting ensemble classifier (SVEC) for the predictive modeling of acute coronary syndrome (ACS) outcomes such as STEMI and NSTEMI.
This study aims to develop an ensemble learning-driven framework as a diagnostic support tool to prevent misdiagnosis.
Sep 15, 2021 · Machine learning models applied to linked administrative data can potentially improve adverse outcome risk prediction.