An Introduction to Recursive Partitioning Using the ... - Mayo Clinic

24 patients revived / 144 not revived
H
HHHHHHHH
X 1 =1
22 / 13
H HHHH
X 1 =2, 3 or 4
2 / 131
H HHHH
X 2 =1
20/5
X 2 =2 or 3
2/8
X 3 =1
2/31
X 3 =2 or 3
0 / 100
Figure 1: Revival data
1 Introduction
This document is an update of a technical report written several years ago at Stanford
[5], and is intended to give a short overview of the methods found in the rpart
routines, which implement many of the ideas found in the CART (Classication and
Regression Trees) book and programs of Breiman, Friedman, Olshen and Stone [1].
Because CART is the trademarked name of a particular software implementation of
these ideas, and tree has been used for the S-plus routines of Clark and Pregibon[2]
a dierent acronym | Recursive PARTitioning or rpart | was chosen.
The rpart programs build classication or regression models of a very general
structure using a two stage procedure; the resulting models can be represented as
binary trees. An example is some preliminary data gathered at Stanford on revival
of cardiac arrest patients by paramedics. The goal is to predict which patients
are revivable in the eld on the basis of fourteen variables known at or near the
time of paramedic arrival, e.g., sex, age, time from arrest to rst care, etc. Since
some patients who are not revived on site are later successfully resuscitated at the
hospital, early identication of these \recalcitrant" cases is of considerable clinical
interest.
The resultant model separated the patients into four groups as shown in gure
1, where
X 1 = initial heart rhythm
1= VF/VT, 2=EMD, 3=Asystole, 4=Other
X 2 = initial response to debrillation
3