Machine learning versus traditional methods for the development of risk stratification scores: a case study using original Canadian Syncope Risk Score data
Abstract
Artificial Intelligence and machine learning (ML) methods are promising for risk-stratification, but the added benefit over traditional statistical methods remains unclear. We compared predictive models developed using machine learning (ML) methods to the Canadian Syncope Risk Score (CSRS), a risk-tool developed with logistic regression for predicting serious adverse events (SAE) after emergency department (ED) disposition for syncope. We used...
Paper Details
Title
Machine learning versus traditional methods for the development of risk stratification scores: a case study using original Canadian Syncope Risk Score data
Published Date
Nov 3, 2021
Volume
17
Issue
4
Pages
1145 - 1153
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