Transcript Datamining@ARTeatV2

Datamining @ ARTreat
Veljko Milutinović
Zoran Babović
Nenad Korolija
Goran Rakočević
Marko Novaković
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]

Agenda
 ARTReat – the project
 Arteriosclerosis – the basics
 Plaque classification
 Hemodynamic analysis
 Data mining for the hemodynamic problem
 Data mining from patent records
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ARTreat – the project
 ARTreat targets at providing a patient-specific computational model
of the cardiovascular system, used to improve the quality of prediction
for the atherosclerosis progression and propagation
into life-threatening events.
 FP7 Large-scale Integrating Project (IP)
 16 partners
 Funding: 10,000,000 €
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Atherosclerosis
 Atherosclerosis is the condition in which an artery wall thickens
as the result of a build-up of fatty materials such as cholesterol
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Artheriosclerotic plaque
 Begins as a fatty streak, an ill-defined yellow lesion–fatty plaque,
develops edges that evolve to fibrous plaques,
whitish lesions with a grumous lipid-rich core
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Plaque components
 Fibrous, Lipid, Calcified, Intra-plaque Hemorrhage
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Plaque classification
 Different types of plaque pose different risks
 Manual plaque classification (done by doctors)
is a difficult task, and is error prone
 Idea: develop an AI algorithm
to distinguish between different types of plaque
 Visual data mining
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Plaque classification (2)
 Developed by Foundation for Research and Technology
 Based on Support Vector Machines
 Looks at images produced by IVUS and MRI
and are hand labeled by physicians
 Up to 90% accurate
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Data mining task in Belgrade
 Two separate paths:
 Data mining from the results of hemodynamic simulations
 Data mining form medical patient records
 Goal:
to provide input regarding the progression of the disease
to be used for medical decision support
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Hemodynamics – the basics
 Study of the flow of blood
through the blood vessels
 Maximum Wall Shear Stress –
an important parameter
for plaque development prognoses
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Hemodynamics - CFD
 Classical methods for hemodynamic calculations
employ Computer Fluid Dynamics (CFD) methods
 Involves solving the Navier-Stokes equation:
 …but involves solving it millions of times!
 One simulation can take weeks
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Data mining form hemodynamic simulations
(first path)
 Idea: use results of previously done simulations
 Train a data mining AI system capable of regression analysis
 Use the system to estimate the desired values
in a much shorter time
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Neural Networks - background
 Systems that are inspired by the principle of operation
of biological neural systems (brain)
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Neural Networks – the basics
 A parallel, distributed information processing structure
 Each processing element has a single output which branches
(“fans out”) into as many collateral connections as desired
 One input, one output and one or more hidden layers
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Artificial neurons
 Each node (neuron) consists of two segments:
 Integration function
 Activation function
 Common activation function
 Sigmoid
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Neural Networks - backpropagation
 A training method for neural networks
 Try to minimize the error function:
by adjusting the weights
 Gradient descent:
 Calculate the “blame” of each input for the output error
 Adjust the weights by:
(γ- the learning rate)
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Input data set
 Carotid artery
 11 geometric parameters and the MWSS value
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The model
 One hidden layer
 Input layer: linear
 Hidden and output:
sigmoid
 Learning rate 0.6
 500K training cycles
 Decay and momentum
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Current results
 Average error: 8.6%
 Maximum error 16,9%
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The “dreaded” line 4
 Line 4 of the original test set proved difficult to predict
 Error was over 30%
 Turned out to be an outlier
 Combination of parameters was such that it couldn’t
 But the CFD worked, NN worked
 Visually the geometry looked fine
 Goes to show how challenging the data preprocessing can be
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Dataset analysis
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Two distinct areas of MWSS values:
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the subset with lower values of MWSS, where a similar clear pattern
can be seen against all of the input variables,
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scattered cloud of values in the subset with higher MWSS values.
Histogram shows the majority of values grouped in the lower half
of the values in the set,
with only a small number of points in the higher half.
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MWSS value prediction
 Two approaches:
 Single model
 Two models:
 one for the low MWSS value data,
 one for higher values,
 classifier to choose the appropriate model
 Models based
on Linear Regression and SVM
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Results
Model
Root square mean error
Correlation coef.
Single model LR
19%
0.7
Single model SVM
17%
0.77
Low value model LR
11%
0.81
Low value model SVM
7%
0.91
High value model LR
42%
0.21
High value model SVM
31%
0.07
Classifier
Correctly classified
Kappa
F measure
SVM
93.2%
0.64
0.517
Poor results for higher values of MWSS
– insufficient values to train a model
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MWSS position
 A few outliers and “strange” values in the data set
 After elimination:
Coordinate
LR
SVM
RSME
CC
RSME
CC
X
0.2389
0.9721
0.277
0.9691
Y
0.1733
0.8953
0.1671
0.9136
Z
0.0736
0.8086
0.1221
0.8304
 Further investigation needed into the data and the “outlier” values,
although it is only a small number of them
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Genetic data
 Single coronary angiography
 Blood chemistry
 Medications
 Single Nucleotide Polymorphism (SNP) data
on selected DNA sequences
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…and now for something
completely different
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Questions
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Datamining @ ARTreat
Project
Veljko Milutinović
Zoran Babović
Nenad Korolija
Goran Rakočević
Marko Novaković
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
