medical diagnosis decision-support system: optimizing pattern recognition of medical data w. art...
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Medical Diagnosis Decision-Support System: Optimizing Pattern Recognition of Medical Data
W. Art Chaovalitwongse Industrial & Systems Engineering
Rutgers University Center for Discrete Mathematics & Theoretical Computer Science (DIMACS)
Center for Advanced Infrastructure & Transportation (CAIT)
Center for Supply Chain Management, Rutgers Business School
This work is supported in part by research grants from NSF CAREER CCF-0546574, and Rutgers Computing Coordination Council (CCC).
Outline
Introduction Classification: Model-Based versus Pattern-Based Medical Diagnosis
Pattern-Based Classification Framework Application in Epilepsy
Seizure (Event) Prediction Identify epilepsy and non-epilepsy patients
Application in Other Diagnosis Data Conclusion and Envisioned Outcome
2
Pattern Recognition:Classification
3
Positive Class
Negative Class?
Supervised learning: A class (category) label for each pattern in the training set is provided.
Model-Based Classification Linear Discriminant Function
Support Vector Machines
Neural Networks
4
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9 No Married 75K No
10 No Single 90K Yes 10
Attributes
Samples
Class or Category
Support Vector Machine
A and B are data matrices of normal and pre-seizure, respectively
e is the vector of ones is a vector of real numbers is a scalar u, v are the misclassification
errors
Mangasarian, Operations Research (1965); Bradley et al., INFORMS J. of Computing (1999)
6
Pattern-Based Classification: Nearest Neighbor Classifiers Basic idea:
If it walks like a duck, quacks like a duck, then it’s probably a duck
Training Records
Test Record
Compute Distance
Choose k of the “nearest” records
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Traditional Nearest Neighbor
X X X
(a) 1-nearest neighbor (b) 2-nearest neighbor (c) 3-nearest neighbor
K-nearest neighbors of a record x are data points that have the k smallest distance to x
Drawbacks
Feature Selection Sensitive to noisy features Optimizing feature selection
n features, 2n combinations combinatorial optimization
Unbalanced Data Biased toward the class (category) with larger
samples Distance weighted nearest neighbors
Pick the k nearest neighbors from each class (category) to the training sample and compare the average distances.
8
Multidimensional Time Series Classification in Medical Data
Positive versus Negative Responsive versus Unresponsive
Multidimensional Time Series Classification
Multisensor medical signals (e.g., EEG, ECG, EMG)
Multivariate is ideal but computationally impossible
It is very common that physicians always use baseline data as a reference for diagnosis The use of baseline data -
naturally lends itself to nearest neighbor classification
Normal
Abnormal?
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Ensemble Classification for Multidimensional time series data Use each electrode as a base classifier Each base classifier makes its own decision Multiple decision makers - How to combine them?
Voting the final decision Averaging the prediction score
Suppose there are 25 base classifiers Each classifier has error rate, = 0.35 Assume classifiers are independent Probability that the ensemble classifier makes a wrong prediction
(voting):
25
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25 06.0)1(25
i
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i
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Modified K-Nearest Neighbor for MDTS
11
Ch 1Ch 2Ch 3
Ch n
……
……
….
D(X,Y)
Time series distances: (1) Euclidean, (2) T-Statistical, (3) Dynamic Time Warping
AbnormalNormal
K = 3
Dynamic Time Warping (DTW)The minimum-distance warp path is the optimal alignment of two time series, where the distance of a warp path W is:
is the Euclidean distance of warp path W.
is the distance between the two data point indices
(from Li and Lj) in the kth element of the warp path.
)(WDist
K
ktksk wwDistWDist
1,, ),()(
),( ,, tksk wwDist
Dynamic Programming:
30,30DThe optimal warping distance is
1,1,1,,,1min,, tsDtsDtsDLLDisttsD tj
si
12Figure B) Is from Keogh and Pazzani, SDM (2001)
Optimizing Pattern Recognition
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Baseline Data
Signal Processing(Feature Extraction)
Extracted Features
Selected Featuresof All Baseline Data
Classifying New Samples
Feature Selection
Cleansed Data
Baseline Data
Signal Processing(Feature Extraction)
Selecting Good Baseline Dataand Deleting Outliers
Integrated Feature Selection & Pattern
Matching Optimization
Classifying New Samples
Optimally Selected Featuresof Optimized Baseline Data
Traditional Pattern-Based Classification Proposed Pattern-Based Classification
Cleansed Data
Extracted Features
Support Feature Machine
Given an unlabeled sample A, we calculate average statistical distances of A↔Normal and A↔Abnormal samples in baseline (training) dataset per electrode (channel).
Statistical distances: Euclidean, T-statistics, Dynamic Time Warping
Combining all electrodes, A will be classified to the group (normal or abnormal) that yields the minimum average statistical distance; or the maximum number of votes
Can we select/optimize the selection of a subset of electrodes that maximizes number of correctly classified samples
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Two distances for each sample at each electrode are calculated: Intra-Class: Average distance from each sample to all other
samples in the same class at Electrode j Inter-Class: Average distance from each sample to all other
samples in different class at Electrode j
Averaging: If for Sample i (on average of selected electrodes)
Average intra-class distance over all electrodes
Average inter-class distance over all electrodes
<
We claim that Sample i is correctly classified.
SFM: Averaging and Voting
Voting: If for Sample i at Electrode j (vote)
Intra-class distance < Inter-class distance (good vote)
Based on selected electrodes, if # of good votes > # of bad votes, then Sample i is correctly classified.
Chaovalitwongse et al., KDD (2007) and Chaovalitwongse et al., Operations Research (forthcoming)
Distance Averaging: Training
Industrial & Systems Engineering Rutgers University
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Sample i at Feature 1
∙∙∙
Sample i at Feature 2 Sample i at Feature m
Select a subset of features ( ) such that as many samples as possible.
m,...,,s 21
sj sj
ijdijd
1id
1id
2id imd
2idimd
Majority Voting: Training
Industrial & Systems Engineering Rutgers University
17
(Correct) if ; (Incorrect) otherwise. 1ija ijdijd
Negative Positive
iijd ijd
Feature j
0ija
Negative Positive
Feature j
jid jid i’
total number of samples.n total number of electrodes.m
SFM Optimization Model
1 if sample is correctly classified;
0 otherwise, for 1,..., .i
iy
i n
1 if electrode is selected;
0 otherwise, for 1,..., .j
jx
j m
average distance from sample to all other samples
in the same class, for 1... and 1... .
ijd i
i n j m
Intra-Class
average distance from sample to all other samples
in different class, for 1... and 1... .
ijd i
i n j m
Inter-Class
Chaovalitwongse et al., KDD (2007) and Chaovalitwongse et al., Operations Research (forthcoming)
1
11 1
21 1
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max
s.t. for 1,...,
1 for 1,...,
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1,...,
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Averaging SFM
Chaovalitwongse et al., KDD (2007) and Chaovalitwongse et al., Operations Research (forthcoming)
Maximize the number of correctly classified samples
Must select at least one electrode
Logical constraints on intra-class and inter-class distances if a sample is correctly classified
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s.t. for 1,...,2
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Voting SFM
Chaovalitwongse et al., KDD (2007) and Chaovalitwongse et al., Operations Research (forthcoming)
Maximize the number of correctly classified samples
Must select at least one electrode
Logical constraints: Must win the voting if a sample is correctly classified
1 if sample is correctly classified at electrode (good vote);
0 otherwise (bad vote), for 1,..., and 1,..., .ij
i ja
i n j m
Precision matrix, A contains elements of
Support Feature Machine
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Step 1: For individual feature (electrode), apply the nearest neighbor rule to every training
sample to construct the distance and accuracy matrices
Step 2: Formulate and solve the SFM models and obtain the optimal feature (electrode)
selection
Step 3: Employ the nearest neighbor rule to classify unlabeled data to the closest baseline
(training) data based on the selected features
(electrodes)
UnlabeledSamples
Training Testing
Abnormal Samples
NormalSamples
x–electrode selectiony– training accuracy
–voting matrix–distance matrices
Support Feature Machine
Support Vector Machine
Feature 1
Feature 2
Feature 3
Ch 1Ch 2Ch 3
Ch n
……
……
….
1 2 3 4 nA data vector of EEG sample
……
Pre-Seizure
Normal
Application in Epilepsy Diagnosis
23
Facts about Epilepsy About 3 million Americans and other 60 million people worldwide (about 1%
of population) suffer from Epilepsy.
Epilepsy is the second most common brain disorder (after stroke), which causes recurrent seizures (not vice versa).
Seizures usually occur spontaneously, in the absence of external triggers.
Epileptic seizures occur when a massive group of neurons in the cerebral cortex suddenly begin to discharge in a highly organized rhythmic pattern.
Seizures cause temporary disturbances of brain functions such as motor control, responsiveness and recall which typically last from seconds to a few minutes.
Based on 1995 estimates, epilepsy imposes an annual economic burden of $12.5 billion* in the U.S. in associated health care costs and losses in employment, wages, and productivity.
Cost per patient ranged from $4,272 for persons** with remission after initial diagnosis and treatment to $138,602 for persons** with intractable and frequent seizures.
*Begley et al., Epilepsia (2000); **Begley et al., Epilepsia (1994). 24
Simplified EEG System and Intracranial Electrode Montage
1 1
1 1
1 1
2 2
2 2
2 2
3 3
3 3
3 3
4 4
4 4
4 4
5 5
LTDRTD
LOF
LST
ROF
RST
1 1
1 1
1 1
2 2
2 2
2 2
3 3
3 3
3 3
4 4
4 4
4 4
5 5
LTDRTD
LOF
LST
ROF
RST
Electroencephalogram (EEG) is a traditional tool for evaluating the physiological state of the brain by measuring voltage potentials produced by brain cells while communicating
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Scalp EEG Acquisition
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Goals: How can we help? Seizure Prediction
Recognizing (data-mining) abnormality patterns in EEG signals preceding seizures
Normal versus Pre-Seizure Alert when pre-seizure samples are detected (online classification) e.g., statistical process control in production system, attack alerts from
sensor data, stock market analysis
EEG Classification: Routine EEG Check Quickly identify if the patients have epilepsy Epilepsy versus Non-Epilepsy Many causes of seizures: Convulsive or other seizure-like activity can be
non-epileptic in origin, and observed in many other medical conditions. These non-epileptic seizures can be hard to differentiate and may lead to misdiagnosis.
e.g., medical check-up, normal and abnormal samples
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Normal versus Pre-Seizure
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10-second EEGs: Seizure EvolutionNormal Pre-Seizure
Seizure Onset Post-Seizure
Chaovalitwongse et al., Annals of Operations Research (2006) 29
Normal versus Pre-SeizureData Set
EEG Dataset Characteristics
Patient ID Seizure types Duration of EEG(days) # of seizures
1 CP, SC 3.55 7
2 CP, GTC, SC 10.93 7
3 CP 8.85 22
4 ,SC 5.93 19
5 CP, SC 13.13 17
6 CP, SC 11.95 17
7 CP, SC 3.11 9
8 CP, SC 6.09 23
9 CP, SC 11.53 20
10 CP 9.65 12
Total 84.71 153
CP: Complex Partial; SC subclinical; GTC: Generalized Tonic/Clonic
Sampling Procedure
Randomly and uniformly sample 3 EEG epochs per seizure from each of normal and pre-seizure states.
For example, Patient 1 has 7 seizures. There are 21 normal and 21 pre-seizure EEG epochs sampled.
Use leave-one(seizure)-out cross validation to perform training and testing.
Seizure Seizure Duration of EEG
30 minutes 30 minutes
8 hours 8 hours 8 hours 8 hoursPre-seizure
Normal
Information/Feature Extraction from EEG Signals Measure the brain dynamics from
EEG signals Apply dynamical measures (based
on chaos theory) to non-overlapping EEG epochs of 10.24 seconds = 2048 points.
Maximum Short-Term Lyapunov Exponent measure the stability/chaoticity of
EEG signals measure the average uncertainty
along the local eigenvectors and phase differences of an attractor in the phase space
Pardalos, Chaovalitwongse, et al., Math Programming (2004)
Time
EE
G V
olt
age
Evaluation Sensitivity measures the fraction of positive cases that
are classified as positive.
Specificity measures the fraction of negative cases classified as negative.
Sensitivity = TP/(TP+FN)Specificity = TN/(TN+FP)
Type I error = 1-Specificity Type II error = 1-Sensitivity
Chaovalitwongse et al., Epilepsy Research (2005)
Leave-One-Seizure-Out Cross Validation
SFM
N2
N3
N4
N5
P2
P3
P4
P5
1234567...
23242526
Training Set
Testing Set
Selected Electrodes
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P1N1
N – EEGs from Normal StateP – EEGs from Pre-Seizure Stateassume there are 5 seizures in the recordings
EEG Classification Support Vector Machine [Chaovalitwongse et al., Annals of OR (2006)]
Project time series data in a high dimensional (feature) space Generate a hyperplane that separates two groups of data – minimizing the
errors Ensemble K-Nearest Neighbor [Chaovalitwongse et al., IEEE SMC: Part A (2007)]
Use each electrode as a base classifier Apply the NN rule using statistical time series distances and optimize the
value of “k” in the training Voting and Averaging
Support Feature Machine [Chaovalitwongse et al., SIGKDD (2007); Chaovalitwongse et al., Operations Research (forthcoming)]
Use each electrode as a base classifier Apply the NN rule to the entire baseline data Optimize by selecting the best group of classifiers (electrodes/features)
Voting: Optimizes the ensemble classification Averaging: Uses the concept of inter-class and intra-class distances (or
prediction scores)
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Performance Characteristics:Upper Bound
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SFM -> Chaovalitwongse et al., SIGKDD (2007); Chaovalitwongse et al., Operations Research (forthcoming)NN -> Chaovalitwongse et al., Annals of Operations Research (2006)
KNN -> Chaovalitwongse et al., IEEE Trans Systems, Man, and Cybernetics: Part A (2007)
Separation of Normal and Pre-Seizure EEGs
From 3 electrodes selected by SFM From 3 electrodes not selected by SFM
Performance Characteristics:Validation
3939
SFM -> Chaovalitwongse et al., SIGKDD (2007); Chaovalitwongse et al., Operations Research (forthcoming)SVM-> Chaovalitwongse et al., Annals of Operations Research (2006)
KNN -> Chaovalitwongse et al., IEEE Trans Systems, Man, and Cybernetics: Part A (2007)
Epilepsy versus Non-Epilepsy
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Epilepsy versus Non-EpilepsyData Set
Routine EEG check: 25-30 minutes of recordings ~ with scalp electrodes
Each sample is 5-minute EEG epoch (30 points of STLmax values). Each sample is in the form of 18 electrodes X 30 points
5 sampled epochs
30 points 30 points
Epilepsy patients Non-Epilepsy patients
Elec 1
…..
150 points(25 minutes)
…..
Elec 2
Elec 17Elec 18
Leave-One-Patient-Out Cross Validation
SFME1
N2
N3
N4
N5
E2
E3
E4
E5
N1
1234567...
23242526
Training Set
Testing Set
Selected Electrodes
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N – Non-EpilepsyP – Epilepsy
Voting SFM: Validation
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0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
k=5 k=7 k=9 k=11 k=All
Ove
rall A
ccura
cyVoting SFM Performance – Average of 10 Patients
DTW
EU
TS
KNN SFM KNN SFM KNN SFM KNN SFM KNN SFM
Averaging SFM: Validation
44
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
k=5 k=7 k=9 k=11 k=All
Ove
rall A
ccura
cy
Averaging SFM Performance – Average of 10 Patients
DTW
EU
TS
KNN SFM KNN SFM KNN SFM KNN SFM KNN SFM
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 180%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Selected Electrodes From Averaging SFM
Averaging SFM - DTW
Averaging SFM - EU
Averaging SFM - TS
Electrode
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Other Medical Diagnosis
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Other Medical Datasets
Breast Cancer Features of Cell Nuclei (Radius, perimeter, smoothness, etc.) Malignant or Benign Tumors
Diabetes Patient Records (Age, body mass index, blood pressure, etc.) Diabetic or Not
Heart Disease General Patient Info, Symptoms (e.g., chest pain), Blood Tests Identify Presence of Heart Disease
Liver Disorders Features of Blood Tests Detect the Presence of Liver Disorders from Excessive Alcohol Consumption
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Performance
LP SVMNLP SVM
V-SFMA-SFM
WDBC 98.0896.17
97.2897.42
HD 85.0684.66
86.4886.92
PID 77.6677.51
75.0177.96
BLD 65.7157.97
63.4666.43
48
LP SVMNLP SVM
V-NNA-NN
V-SFMA-SFM
97.0095.38
91.6093.18
94.9996.01
82.9683.94
80.8782.77
82.4984.92
76.9376.09
63.1474.94
72.7575.83
65.7157.97
38.3854.09
58.2059.57
Training Testing
Average Number of Selected Features
LP SVMNLP SVM
V-SFMA-SFM
WDBC 3030
11.68.5
HD 1313
7.48.7
PID 88
4.34.5
BLD 66
3.33.7
49
Medical Data Signal Processing Apparatus (MeDSPA) Quantitative analyses of medical data
Neurophysiological data (e.g., EEG, fMRI) acquired during brain diagnosis
Envisioned to be an automated decision-support system configured to accept input medical signal data (associated with a spatial position or feature) and provide measurement data to help physicians obtain a more confident diagnosis outcome.
To improve the current medical diagnosis and prognosis by assisting the physicians recognizing (data-mining) abnormality patterns in medical data recommending the diagnosis outcome (e.g., normal or abnormal) identifying a graphical indication (or feature) of abnormality (localization)
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Automated Abnormality Detection Paradigm
User/Patient
InterfaceTechnology
MultichannelBrain Activity
Data Acquisition
Statistical Analysis:Pattern Recognition
Initiate a warning or a variety of therapies (e.g., electrical stimulation, drug injection)
Stimulator
Drug
Optimization: Feature Extraction/ Clustering
Nurse
Feature 1
Feature 2
Feature 3
Acknowledgement: Collaborators
E. Micheli-Tzanakou, PhD L.D. Iasemidis, PhD R.C. Sachdeo, MD R.M. Lehman, MD B.Y. Wu, MD, PhD
Students Y.J. Fan, MS Other undergrad students
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Thank you for your attention!
Questions?
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