Datamining @ ARTreat
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Transcript of Datamining @ ARTreat
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Datamining @ ARTreat
Veljko Milutinović [email protected] Babović [email protected] Korolija [email protected] Rakočević [email protected] Novaković [email protected]
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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 modelof the cardiovascular system, used to improve the quality of predictionfor 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 algorithmto 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 MRIand 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 diseaseto 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 parameterfor 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 valuesin a much shorter time
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Neural Networks - background
Systems that are inspired by the principle of operationof 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 Two distinct areas of MWSS values:
the subset with lower values of MWSS, where a similar clear pattern can be seen against all of the input variables,
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
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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:
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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 [email protected] Babović [email protected] Korolija [email protected] Rakočević [email protected] Novaković [email protected]