Abstract
Data files that assess the effect of various predictors on frequency counts of morbidities/mortalities can be classified into multiple cells with varying incident risks (like, e.g., the incident risk of infarction). The underneath table gives an example:
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This chapter was previously published in “Machine learning in medicine-cookbook 3” as Chap. 5, 2014.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsAuthor information
Authors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Cleophas, T.J., Zwinderman, A.H. (2015). Loglinear Models for Assessing Incident Rates with Varying Incident Risks (12 Populations). In: Machine Learning in Medicine - a Complete Overview. Springer, Cham. https://doi.org/10.1007/978-3-319-15195-3_38
Download citation
DOI: https://doi.org/10.1007/978-3-319-15195-3_38
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-15194-6
Online ISBN: 978-3-319-15195-3
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)