Abstract
Pre-requisites to better understand the chapter: knowledge of the major steps and procedures of developing a clinical prediction model. Logical position of the chapter with respect to the previous chapter: in the last chapters, you have learned how to develop and validate a clinical prediction model. You have been learning logistic regression as main algorithm to build the model. However, several different more complex algorithms can be used to build a clinical prediction model. In this chapter, the main machine learning based algorithms will be presented to you. Learning objectives: you will be presented with the definitions of: machine learning, supervised and unsupervised learning. The major algorithms for the last two categories will be introduced.
| Original language | English |
|---|---|
| Title of host publication | Fundamentals of Clinical Data Science |
| Editors | Pieters Kubben, Michel Dumontier, Andre Dekker |
| Publisher | Springer |
| Chapter | 9 |
| Pages | 121-133 |
| Number of pages | 13 |
| ISBN (Electronic) | 978-3-319-99713-1 |
| ISBN (Print) | 978-3-319-99712-4 |
| DOIs | |
| Publication status | Published - 2019 |
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