Content Motivation About WinMiner

Content

• Motivation

• About WinMiner

• Data Mining Effort

• Conclusion

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

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?

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

About WinMiner

• Mining context

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

About WinMiner

• Selecting the optimal window span

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

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

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

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

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.

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. "

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

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