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Mining Episode Rules in STULONG dataset

• N. Méger1, C. Leschi1, N. Lucas2 C. Rigotti1
• 1 INSA Lyon - LIRIS FRE CNRS 2672
• 2 Université dOrsay LRI CNRS UMR 8623

This work has been partially funded by the
European Project AEGIS (IST-2000-26450).
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Content

• Motivation
• About WinMiner
• Data Mining Effort
• Conclusion

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Motivation Data

• STULONG Data A 20 year longitudinal study of
  risk factors related to atherosclerosis in a
  population of middle-aged men
• Tables ENTRY and CONTROL
• 1216 patients described by
• 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

• identified risks factors
• no treatment available
• necessity to consider a global risk instead of
  concentrating prevention efforts on individual
  ones
• 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

• WinMiner a single optimised way to find
  sequential patterns in data along with their
  optimal time intervals, under user constraints
• WinMiner suggests to experts possible temporal
  dependencies among occurrences of event types
• WinMiner outputs "small" collections of
  sequential patterns

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About WinMiner

• Mining context

• large event sequences
• episode episode rules

A
B
A
B
C
A
B
C
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About WinMiner

• Selecting patterns
• support how many times an episode/episode rule
  occurs within an event sequence?
• A ? B 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
• patterns are selected using
• a minimum support threshold
• a minimum confidence threshold

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About WinMiner

• Selecting the optimal window span

confidence
First Local Maximum (FLM)
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

• 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

• Mainly focused on table CONTROL (1226
  patients/10572 examinations)
• Joint operations to export information from table
  ENTRY
• Categorization of some factors
• Choice of relevant factors according to
• Medical expertise
• Mining approach
• ? Table Contr_Mod_2

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DM Effort Data preprocessing

• Important factors (according to medical experts)
  
• cholesterol
• hypertension
• smoking
• physical activity
• age
• diabetes
• alcohol consumption
• BMI
• family anamnesis
• level of education

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DM Effort Data preprocessing

• Contr_mod_2 ?? large event sequence
• For each patient a subsequence containing all
  his control examinations
• Coding guarantees that events corresponding to 2
  different patients can not be associated in the
  same episode rule
• Large event sequence concatenation of all sub
  sequences constructed for patients.

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DM effort Results

• 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

• Well known phenomena
• indication about correctness in pre-processing as
  well as in mining data
• Added-value suggestion concerning their temporal
  aspects
• 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

• With STULONG data Searching for temporal
  dependencies between atherosclerosis risk factors
  and clinical demonstration of atherosclerosis
  that have an optimal interval/window size
• 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
• A few episode rules obtained, that allows experts
  to manually analyse the outputs
• Could be applied to other medical data sets to
  help in finding unknown phenomena
• ? New perspectives both for data
  miners and physicians