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lecture6 dec trees

Healthcare Decision Support Systems
Lecture 6: Decision Trees
Lecturer: Prof Jim Warren
HLTHINFO 730 – lecture 6 Slide #1
Decision Trees
• Essentially flowcharts
– A natural order of ‘micro decisions’ (Boolean –
yes/no decisions) to reach a conclusion
– In simplest form all you need is
• A start (marked with an oval)
• A cascade of Boolean decisions (each with exactly
outbound branches)
• A set of decision nodes (marked with ovals) and
representing all the ‘leaves’ of the decision tree (no
outbound branches)
HLTHINFO 730 – lecture 6 Slide #2
Consider this fragment of the ‘Prostate Cancer Workup (Evaluation)’
decision tree from
The page also shows supporting text:
“Additional testing is recommended for men
expected to live 5 or more years or who have
symptoms from the cancer. For example, if
the tumor is T1 or T2, a bone scan is
recommended if the PSA level is greater than
20 or if the Gleason score is greater than 8. A
bone scan is also recommended if the man
has any symptoms, or the cancer is growing
outside the prostate (T3 or T4). A CT or MRI
of the pelvis is recommended when the tumor
is T1 or T2 and there is a 7% or greater
chance of lymph node spread based on the
Partin tables, or the tumor is growing outside
the prostate (T3 or T4).”
HLTHINFO 730 – lecture 6 Slide #3
KE problems for flowchart
• The natural language may pack a lot in
– E.g., “any one of the following”
– Even harder if they say “two or more of the following” which
implies they mean to compute some score and then ask if it’s
• Incompleteness
– There are logically possible (and, worse, physically possible)
cases that aren’t handled
• The ‘for example’ in the text is a worry
• Inconsistency
– Are we trying to reach one decision (which test) or a set of
• 1) whether to do a bone scan
• 2) whether to do a ‘CT or MRI’
HLTHINFO 730 – lecture 6 Slide #4
Let’s try it anyway
• What’s said for ‘staging workup’ looks like this
T1 or T2
T3 or T4
T3 or T4
S2 = step 2: ‘CT or MRI of pelvis
BS = Bone scan
S3 = step 3: ‘All others: no
additional testing’
LNS = ‘7% or greater chance of
lymph node spread based on
the Partin tables’
Please don’t decide your father’s
Prostate follow-up from this!
It’s unverified, and I don’t think a
tumour can be ‘T1 or T2’ and
ALSO ‘T3 or T4’ (but that’s what
it says!)
HLTHINFO 730 – lecture 6 Slide #5
Decision Tables
• As you can see from the Prostate example, a
flowchart can get huge
– We can pack more into a smaller space if we
relinquish some control on indicating the order of
• A decision table has
– One row per ‘rule’
– One column per decision variable
– An additional column for the decision to take when
that rule evaluates to true
HLTHINFO 730 – lecture 6 Slide #6
Decision Table example
(True or
From van Bemmel & Musen, Ch 15
HLTHINFO 730 – lecture 6 Slide #7
Flowcharts v. Tables
• Decision table is not as natural as a flowchart
– But we’ve seen, a ‘real’ (complete and consistent) flowchart ends
up very large (or representing a very small decision)
• Decision table gets us close to production rule
– Good as design specification to take to an expert system shell
• Completeness is more evident with a flowchart
• Decision table could allow for multiple rules to
simultaneously evaluate to true
– Messy on a flowchart (need multiple charts, or terminals that
include every possible combination of decision outcomes)
• Applying either in practice requires KE in a broad sense
– E.g., may need to reformulate the goals of the guideline
HLTHINFO 730 – lecture 6 Slide #8
On to production rule systems
• In a production rule system we have decision-table-like
rule, but also the decision outcomes can feed back to the
decision variables
• Evaluating some special decision rule (or rules) is then
the goal for the decision process
– The other rules are intermediary, and might be part of the
explanation of how externally-derived decision variables were
used to reach a goal decision
• The inference engine of the expert system shell chooses
how to reach the goal
– i.e., with backward chaining, or forward chaining
– Possibly with some direction from a User Interface (UI) manager
component (e.g., we might group sets of variables for input into
forms as a web page)
HLTHINFO 730 – lecture 6 Slide #9
Boolean Algebra
• To formulate flow chart decisions and
(especially) decision table rows, can help to
have mastered Boolean Algebra
• Basic operators
– NOT – if A was true, NOT A is false
– AND – A AND B is only true if both A and B are true
– OR – A OR B is true if either A, or B, or both are true
(aka inclusive or)
• This is not the place for a course on Boolean
algebra, but a few ideas will help…
HLTHINFO 730 – lecture 6 Slide #10
• Alas there are a lot of ways the operators are written
– NOT A might appear as A, ~A, A′ or ¬A
– A AND B might appear as A.B, A·B, A^B or simply AB
– A OR B might appear as A+B or AvB
• We can use parentheses like in normal algebra
– C(A+B) means the expression is True if and only if C is true AND
either B is true OR C is true (or both)
– It’s equivalent to CA + CB (C-AND-A or C-AND-B, evaluate AND
before OR)
– So AND is a bit like multiplication, whereas OR is a bit like
• 1 + 1 ≥ 1 1 + 0 ≥ 1 (inclusive OR)
• 1 x 1 ≥ 1 1 x 0 ≥ 1 (logical AND)
HLTHINFO 730 – lecture 6 Slide #11
• If you just keep your head and focus on
the meaning in the clinical domain, you
can usually find the Boolean expression
you need
– Be sure to be precise
• “NOT (x>43)” is “x is NOT GREATER than 43” is
“x<=43” (get your equals in the right place!)
(with this advice, I won’t teach you De Morgan’s
Law, truth tables, or Karnaugh maps, but feel
free to look them up – they all Google well)
HLTHINFO 730 – lecture 6 Slide #12
Venn diagrams
• Visual representations of membership in sets
– Can be very useful to decide what Boolean
expression you need
– Say A is the set of everything with two legs and B the
set of everything
A: 2 legs B: can fly
that flies
• A^B would be true for a parrot
• A would be true for a human,
B would be false
• B would be true for a mosquito,
A would be false
HLTHINFO 730 – lecture 6 Slide #13
Decision Tree Induction
• An alternative to knowledge engineering a
decision tree is to turn the task over to a
machine learning algorithm
– The decision tree can be ‘induced’ (or inducted) from
a sufficiently large set of example
• The ID3 algorithm is the classic for inducing a
decision tree using Information Theory
– If I have 50 examples where the patients survived and
50 where they didn’t I have total (1.0) entropy and
zero information
– Given a set of potential decision attributes I can try to
create more order (less entropy, more information) in
the data
HLTHINFO 730 – lecture 6 Slide #14
Example: Induced Decision Tree
Of course they go and use
ovals for listing the decision
variables, put the test criteria
on the arcs and put ‘leaf’
decisions in rectangles –
notations vary; get used to it!
From Chen et al, Complete Blood Count as a Surrogate CD4 Marker for HIV Monitoring in
Resource-limited Settings, 10th Conf on Retroviruses and Opportunistic Infection, 2003.
HLTHINFO 730 – lecture 6 Slide #15
Using Entropy measures in ID3
• For a decision node S with pp positive example (e.g.,
surviving patients) and pn negataive example
– Entropy(S) = - pplog2 pp – pnlog2 pn
• So with 15 survivors out of 25 patients
– Entropy(S) = - (15/25) log2 (15/25) - (10/25) log2 (10/25) = 0.970
• I want to select a Boolean attribute A that splits S such
that the two subsets are as ordered as possible, usually
HLTHINFO 730 – lecture 6 Slide #16
ID3 continued
• So if I have 20 available Boolean decision variables
– I try splitting my cases, S, according to each, until I find the
variable that gives the most Gain
– I repeat this on each sub-tree until either every node if perfect
(all survivors, or all deaths) or I run out of attributes
• If my variables aren’t Boolean, then I have more work to
– Actually, the Gain equation works fine if the attribute is multivalued (Day of Week would be OK, I just have a 7-way split in
my tree)
– For continuous values I have to ‘discretize’ – make one or more
split points
• e.g., SBP<140? – now I’ve made continuous-valued blood pressure
into a Boolean
• Can be done based on knowledge (e.g., clinical significance), or
handed to an algorithm to search for the max Gain
See http://dms.irb.hr/tutorial/tut_dtrees.php
HLTHINFO 730 – lecture 6 Slide #17
• You don’t find ‘pure’ ID3 too much
– Other algorithms in a similar spirit to search for are
C4.5 and Adaboost
• Tools
– Matlab implements decision tree induction
– Weka toolkit (from Waikato Uni) has a variety of Java
tools for machine learning
– Try Pierre Geurts’ online decision tree induction
applet, e.g., for ‘animal descriptions’ from
HLTHINFO 730 – lecture 6 Slide #18
I de-selected
from the
attributes, hit
New Tree,
then Build,
and hit
Zoom+ a
couple times
(note that the
order in the
effects how
the decision
nodes end up
HLTHINFO 730 – lecture 6 Slide #19
• Decision trees are a basic design-level knowledge
representation technique for ‘logical’ (rule based,
Boolean-predicate-driven) decisions
• Decision tables let you compactly compile a host of
decisions on a fixed set of decision variables
– These take you very close to the representation needed to
encode production rules for an inference engine
• Rule induction from data provides an alternative to
conventional Knowledge Engineering
– Computer figures out rules that fit past decisions instead of you
pursuing experts to ask them what rules they use
HLTHINFO 730 – lecture 6 Slide #20
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