A Gradient Boosting Machine Learning Model for Predicting Early Mortality in the Emergency Department Triage: Devising a Nine-Point Triage Score

By M. Klug, Y. Barash, Sigalit Bechler, Yehezkel S. Resheff, Talia Tron, A. Ironi, S. Soffer, E. Zimlichman, Eyal Klang. 2019

109 citations recorded by the source corpus.

Subfields: Emergency Medicine, Epidemiology, Health Informatics

How researchers see this paper

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

In Emergency Medicine

1. Nan Liu
2. Marcus Eng Hock Ong
3. Feng Xie
4. Benjamin Alan Goldstein
5. Bibhas Chakraborty

In Epidemiology

1. Nan Liu
2. Marcus Eng Hock Ong
3. Feng Xie
4. Benjamin Alan Goldstein
5. Bibhas Chakraborty

Citations in the corpus

• AutoScore: A Machine Learning–Based Automatic Clinical Score Generator and Its Application to Mortality Prediction Using Electronic Health Records (2020, background): “Although numerous machine learning models, such as neural networks [41,42] and ensemble learning models [43,44], have been developed to complement traditional regression models, most of them are black boxes that do not explain their predictions in a way that humans can understand.”