atherex: an expert system for atherosclerosis risk assessment petr berka, vladimír laš university...
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AtherEx: an Expert System for
Atherosclerosis Risk Assessment
Petr Berka, Vladimír LašUniversity of Economics, Prague
Marie Tomečková Institute of Computer Science,
Prague berka@vse.cz
AIME 2005 2
Atherosclerosis
slow buildup of deposits of fatty substances, cholesterol, body cellular waste products, calcium, and fibrin (a clotting material in the blood) in the inside lining of an artery. The buildup (refered as a plaque) with the formation of the blood clot (thrombus) on the surface of the plaque can partially or totally block the flow of blood through the artery. If either of these events occurs and blocks the entire artery, a heart attack or stroke or other lifethreatening events may result.
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Risk Factors of Atherosclerosis
non-affectable: sex, age, family history
affectable: blood pressure, level of cholesterol, smoking,
factors of life style nourishment (obesity) physical activities reaction on stress
many other factors
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Cardiovascular Disease (CVD) Risk Calculators
system questions suitable for results
NCEP ATP III 11 + 2 all patients CVD risk in 10 years
Risk assessment tool
4 + 2 all patients IM risk in 10 years
Framingham Risk Assessment
5 + 2 all patients IM risk in 10 years
PROCAM Risk Calculator
6 + 3 middle-aged men IM risk in 10 years
PROCAM Risk Score
7 + 4 middle-aged men IM risk or death on CVD in 10 years
PROCAM Neural Net
11 + 5 middle-aged men IM risk in 10 years
Heart Score 4 + 2 middle-aged patients death on CVD in 10 years
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CVD Calculators Expert Systems
Calculators evaluate risk as weighted sum of all
factors user must give exact answers to all
questions Expert Systems
evaluate risk by inference in a rule base can handle uncertain or missing
information
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Expert System NEST (1/2) Knowledge representation
attributes (binary, nominal, numeric) and propositions
rules: condition conclusion (weight), action compositional - each literal in conclusion has a weight apriori - compositional rules without condition logical - non-compositional rules without weights
Inference as a combination of backward and forward chaining compositional inference for compositional and
apriori rules (combining contributions of rules) non-compositional inference for logical rules
(modus ponens + disjunction)
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Expert System NEST (2/2) Uncertainty processing
uncertainty possible in both expert’s knowledge and in user’s answers during consultation,
compositional approach (combining contributions of all applicable rules) based on algebraic theory of P. Hájek
different sets of combination functions (MYCIN + PROSPECTOR like, Lukasiewicz many-valued logic, neural networks like)
two basic modes of consultation: dialogue and questionnaire,
implemented as stand-alone or client-server version. http://lisp.vse.cz/NEST
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Basics of the AtherEx System
Knowledge Base created in two-step process machine learning algorithm applied to data from
an epidemiological study of atherosclerosis prevention
obtained rules revised and refined by an expert system works mainly with risk factors easily
understandable by non expert users (20 factors + 1 lab. test)
result of consultation is the classification of a patient into one of four groups w.r.t atherosclerosis risk.
http://j116h1.vse.cz
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Atherosclerosis risk factors study
Longitudinal (1975-2000) study of atherosclerosis risk factors in the population of middle-aged men divided into three groups (normal, risk, pathological).
to identify atherosclerosis risk factors prevalence in a population of middle-aged men,
to follow the development of these risk factors and their impact on the examined men health, especially with respect to atherosclerotic CVD,
to study the impact of complex risk factors intervention on development of risk factors and CVD mortality,
to compare (after 10-12 years) risk factors profile and health of the selected men in different groups.
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Data STULONG
Entry
1417x64
Control
10572x66
Letter
403x62
Death
389x5
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Rule induction algorithm KEX
Decision rules in the form Ant Class (w)
Compositional algorithm Building rules by a knowledge refinement
process (add new - more specific - rule only if it will improve the classification)
Applying rules by combining contributions of all relevant rules using a pseudo-bayesian formula:
)1()1( 2121
2121 wwww
wwww
http://lispminer.vse.cz
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STULONG ENTRY table analyses (1/2)
classification based only on already known risk factors,
classification based on attributes concerning life style, personal and family history (but without special laboratory tests),
classification based on attributes concerning life style and family history,
classification based only on attributes concerning life style.
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STULONG ENTRY table analyses (2/2)
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Modifications suggested by Domain Expert and Expert
users use the goals "no risk", "low risk",
"medium risk" and "high risk" instead of original groups taken from data,
add rules for remaining values of an attribute, if at least one value of this attribute occur in rules obtained from data,
add the attribute "total cholesterol" and the respective rules,
split some questions.
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Implementation client-server version of NEST used
(client is a web browser) front-end hides details about inference
and uncertainty processing (the developer can design the layout of dialogue for each knowledge base)
dialogue mode of consultation (with the possibility to change answers after consultation using questionnaire)
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Screenshot of AtherEx (1/2)
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Screenshot of AtherEx (2/2)
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Conclusions and future work
We developed a system that should help non-expert users to determine their atherosclerosis risk
The system can infer a conclusion from incomplete and/or uncertain input information
Our experiments have shown that the information about life style can substitute laboratory tests
We plan to include knowledge dealing with the dynamics of the risk factors
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