Categorizing continuous variables resulted in different predictors in a prognostic model for nonspecific neck pain.

By J. Schellingerhout, M. Heymans, HC de Vet, B. Koes, A. Verhagen. 2009

35 citations recorded by the source corpus.

Subfields: Pharmacology, Statistics and Probability

How researchers see this paper

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

In Statistics and Probability

1. A. Glenz
2. Nan Liu
3. Marcus Eng Hock Ong
4. Feng Xie
5. Benjamin Alan Goldstein
6. Bertolt Meyer
7. Bibhas Chakraborty
8. Eric B. Laber

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): “Discretizing features requires a smaller memory footprint, simplifies model interpretation, and can be applied directly by a human expert in routine care [37].”
• Team Faultline Measures (2013, background): “Shaw (2004) argued that the conversion of numeric attributes such as age into categories such as young, middle aged, and old is justified because humans tend to experience continuous variables in categories.”