Transcript presentation

Mining Episode Rules in STULONG
dataset
•N. Méger1, C. Leschi1, N. Lucas2 & C. Rigotti1
1 INSA Lyon
- LIRIS FRE CNRS 2672
2 Université d’Orsay – LRI CNRS UMR 8623
This work has been partially funded by the European Project AEGIS (IST-2000-26450).
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Content
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Motivation
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About WinMiner
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Data Mining Effort
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Conclusion
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Motivation : Data
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STULONG Data : A 20 year longitudinal study of risk factors related
to atherosclerosis in a population of middle-aged men
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Tables ENTRY and CONTROL:
– 1216 patients described by:
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Identification and social characteristics
Behavior
Health events
Physical and biochemical examinations
– From 1 up to 21 control per patients
 A sequence of controls for each patient
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Motivation: Medical issues
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identified risks factors
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no treatment available
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necessity to consider a global risk instead of concentrating prevention
efforts on individual ones
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risk comportments dramatically increases cardio-vascular disease
emergence, but no one knows when
 Relations between risk factors and clinical demonstration of
atherosclerosis?
 Time intervals over which these relations are valid?
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Motivation: WinMiner
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WinMiner: a single optimised way to find sequential
patterns in data along with their optimal time
intervals, under user constraints
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WinMiner suggests to experts possible temporal
dependencies among occurrences of event types
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WinMiner outputs "small" collections of sequential
patterns
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About WinMiner
Mining context
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large event sequences
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episode & episode rules
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B
A
A
B
B
C
C
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About WinMiner
Selecting patterns
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support: how many times an episode/episode rule occurs within
an event sequence?
AB
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ABC
confidence: what is the probability of the RHS of an episode rule
to occur knowing that its LHS already occured?
ABC
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patterns are selected using:
– a minimum support threshold
– a minimum confidence threshold
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About WinMiner

Selecting the optimal window span
C1
confidence
C2 <= C1 - C1*decRate
C2
minimum
confidence
First Local Maximum
(FLM)
optimal window span
window span such
that the episode rule
is frequent
w
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About WinMiner

WinMiner :
– checks all possible episode rules satisfying to
frequency and confidence thresholds
– outputs only the FLM-rules, along with their
respective optimal window sizes
– uses a maximal gap constraint
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DM effort: Aims
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Give to the medical expert:
a mean to follow both the evolution of risk factors
and:
(1) impact of medical intervention
(2) modifications in patients’ behavior
in addition:
– significant time periods of observation
– frequency
– probability
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DM effort: Data preprocessing
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Mainly focused on table CONTROL (1226 patients/10572
examinations)
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Joint operations to export information from table ENTRY
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Categorization of some factors
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Choice of relevant factors according to:
– Medical expertise
– Mining approach
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Table Contr_Mod_2
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DM Effort: Data preprocessing
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Important factors (according to medical experts):
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cholesterol
hypertension
smoking
physical activity
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age
diabetes
alcohol consumption
BMI
family anamnesis
– level of education
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DM Effort: Data preprocessing
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Contr_mod_2  large event sequence
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For each patient: a subsequence containing all his control
examinations
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Coding guarantees that events corresponding to 2 different
patients can not be associated in the same episode rule
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Large event sequence: concatenation of all sub sequences
constructed for patients.
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DM effort: Results
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Examples:
– "If the patient has no hypercholesterolemia, and if
he sometimes follows his diet, then the patient has
no hypercholesterolemia with a probability of 0.8
within 40 months. This rule is supported by 201
examples in the event sequence."
– " If one eats less of fats and carbohydrates and he
has claudication observed some time later, then
this claudication does not disappear with a
probability of 0.8 over 30 months. This rule is
supported by 21 examples. "
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DM effort: Results
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Well known phenomena:
– indication about correctness in pre-processing as well as in
mining data
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Added-value: suggestion concerning their temporal aspects
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To be expected:
– with new data and new risk factors put in evidence in the last
decade, discovering new phenomena along with their
optimal window sizes
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Conclusion
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With STULONG data: Searching for temporal dependencies between
atherosclerosis risk factors and clinical demonstration of
atherosclerosis that have an optimal interval/window size
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Offers to the medical expert a possibility to explicit impact of a risk
factor and to refine its part in comparison with other ones within a time
interval
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A few episode rules obtained, that allows experts to manually analyse
the outputs
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Could be applied to other medical data sets to help in finding unknown
phenomena
 New perspectives both for data miners and
physicians
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