Development and validation of the Emergency Department Assessment of Chest pain Score and 2 h accelerated diagnostic protocol

By M. Than, Dylan F Flaws, Sharon Sanders, J. Doust, P. Glasziou, J. Kline, S. Aldous, R. Troughton, C. Reid, W. Parsonage and 13 more. 2014

238 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, background): “Since then, many scoring systems have been designed for healthcare (Knaus et al., 1981, 1985, 1991; Bone et al., 1992; Le Gall et al., 1993; Antman et al., 2000; Gage et al., 2001; Moreno et al., 2005; Six et al., 2008; Weathers et al., 2013; Than et al., 2014).”
Learning Optimized Risk Scores (2016, methodology): “This is partly because risk scores are often used in applications where models must satisfy requirements related to qualities such as interpretability, usability, or fairness (see e.g., requirements on “face validity” and “user friendliness” in Than et al., 2014).”
A novel cardiovascular risk stratification model incorporating ECG and heart rate variability for patients presenting to the emergency department with chest pain (2016, background): “For example, the history, ECG, age, risk factors (HEART) score [45] and the Emergency Department assessment of chest pain score (EDACS) score [46] could serve as better benchmarks due to their applicability to, and specific design for, an ED setting in which the risk of 30-day MACE needs to be asses”