Visual Data Mining Techniques and Software for Functional …symanzik/talks/2013_ENAR.pdf ·...
Transcript of Visual Data Mining Techniques and Software for Functional …symanzik/talks/2013_ENAR.pdf ·...
Introduction ActiVis Feature 1 ActiVis Feature 2 ActiVis Feature 3 Conclusion & Future Work Appendix
Visual Data Mining Techniques and Software forFunctional Actigraphy Data
Jürgen Symanzik & Abbass SharifDepartment of Mathematics and Statistics
Utah State Universitye–mail: [email protected]
March 12, 2013
Jürgen Symanzik — March 12, 2013 ENAR 2013 Spring Meeting, Orlando, Florida 1
Introduction ActiVis Feature 1 ActiVis Feature 2 ActiVis Feature 3 Conclusion & Future Work Appendix
Outline
1 Introduction
2 ActiVis Feature 1
3 ActiVis Feature 2
4 ActiVis Feature 3
5 Conclusion & Future Work
6 Appendix
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Introduction ActiVis Feature 1 ActiVis Feature 2 ActiVis Feature 3 Conclusion & Future Work Appendix
What is Actigraphy?
Actigraphy: emerging technology for measuring a person’sactivity level continuously over time
Actigraph: watch-like device (attached to the wrist or leg) thatuses an accelerometer to measure (human) movements (everyminute or more often)
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Introduction ActiVis Feature 1 ActiVis Feature 2 ActiVis Feature 3 Conclusion & Future Work Appendix
Actigraph
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Introduction ActiVis Feature 1 ActiVis Feature 2 ActiVis Feature 3 Conclusion & Future Work Appendix
Human Actigraphy
Detecting sleep patterns, and insomnia
Assessing restless leg syndrome
Tracking recovery after heart attacks
Assessing overall status of HIV patients
Assessing depression
Etc.
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Current Visualizations of Actigraphy Data: Actogram & Histogram
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Current Visualizations of Actigraphy Data: Scatterplot (Messy!)
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Functional Data Analysis for Actigraphy Data
Actigraphy data can be best described as functional data
Functional Data Analysis (FDA): “The basic philosophy offunctional data analysis is to think of observed data functions assingle entities, rather than merely as a sequence of individualobservations." (Ramsay & Silverman, 2006)
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Introduction ActiVis Feature 1 ActiVis Feature 2 ActiVis Feature 3 Conclusion & Future Work Appendix
The ActiVis R Package
Feature 1:New Visualization Tools for Actigraphy Data
Feature 2:Integration of New and Current Visualization Tools UsingObject–oriented Model Design
Feature 3:User–friendly Web Interface
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Introduction ActiVis Feature 1 ActiVis Feature 2 ActiVis Feature 3 Conclusion & Future Work Appendix
Feature 1: New Visualization Tools for Actigraphy Data
One Patient
Multiple Patients
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Enhanced Visualizations of Actigraphy Data: One Patient
(a)− Patient X Raw Data Plot
Time
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0500
100015002000250030003500
Activity
Level
● Day 3Day 4Day 5Day 6Avg.
(b)− Patient X Smoothed Data Plot
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(c)− Patient X Velocity (First Derivative) of Smoothed Daily Data
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−6−4−2
0246
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of Actig
raphy Day 3
Day 4Day 5Day 6Avg.
(d)− Patient X Acceleration (Second Derivative) of Smoothed Daily Data
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0.00.10.20.3
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ation of
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phy Day 3Day 4Day 5Day 6Avg.
(e)− Patient X Cumulative Sums Plot
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ands) Day 3
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(f)− Patient X Sorted Cumulative Sums Plot
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n thous
ands) Day 3
Day 4Day 5Day 6Avg.
Jürgen Symanzik — March 12, 2013 ENAR 2013 Spring Meeting, Orlando, Florida 13
Introduction ActiVis Feature 1 ActiVis Feature 2 ActiVis Feature 3 Conclusion & Future Work Appendix
New Visualization Techniques for Actigraphy Data: MultiplePatients
Density–based plots
Data enveloping
Data summing
Multivariate time series plots
Combination of plots (most effective)
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Actigraphy Data
55 patientsTwo types of data
• Actigraphy level data• Depression level data (PHQ–9 scale)
Data collection rate: every 15 seconds or every minute, i.e.,about 6,000 or 1,500 measurements per day (× 3–8 days perpatient × 55 patients)Patient demographics
• Gender: 17 males, and 38 females• Depression level: 15 patients with no depression (Level 0),
13 with mild depression (Level 1), 15 with moderate depression(Level 2), 8 with moderately severe depression (Level 3), and4 with severe depression (Level 4)
Jürgen Symanzik — March 12, 2013 ENAR 2013 Spring Meeting, Orlando, Florida 15
Introduction ActiVis Feature 1 ActiVis Feature 2 ActiVis Feature 3 Conclusion & Future Work Appendix
Density–based Plots: Multiple Patients
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Introduction ActiVis Feature 1 ActiVis Feature 2 ActiVis Feature 3 Conclusion & Future Work Appendix
Data Enveloping: Multiple Patients
Envelope (Min−Max) Plot−Actigraphy Data Aggregation = 1 mins
Time
Activ
ity Le
vel (i
n tho
usan
ds)
12 AM 6 AM 12 PM 6 PM 12 AM
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1015
20 MalesFemales
Envelope (40%−60%) Plot−Actigraphy Data Aggregation = 1 mins
Time
Activ
ity Le
vel
12 AM 6 AM 12 PM 6 PM 12 AM
025
050
075
010
00 MalesFemales
Jürgen Symanzik — March 12, 2013 ENAR 2013 Spring Meeting, Orlando, Florida 17
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Data Summing: Multiple Patients
Envelope (Min−Max) Plot−Actigraphy Data Aggregation = 20 mins
Time
Activ
ity Le
vel (i
n tho
usan
ds)
12 AM 6 AM 12 PM 6 PM 12 AM
010
020
030
0
MalesFemales
Envelope (40%−60%) Plot−Actigraphy Data Aggregation = 20 mins
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ity Le
vel
12 AM 6 AM 12 PM 6 PM 12 AM
030
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0090
0012
000
MalesFemales
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Multivariate Time Series Plots: Multiple Patients
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Introduction ActiVis Feature 1 ActiVis Feature 2 ActiVis Feature 3 Conclusion & Future Work Appendix
Density–based Plots of Cumulative Sums: Multiple Patients
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Envelope Plots of Cumulative Sums: Multiple Patients
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What is Object–oriented Programming (OOP)?
Objects: encapsulate state information and control behaviorClasses: describe general properties for groups of objectsInheritance: new classes can be defined in terms of existingclassesPolymorphism: a function/method has different behaviorsdepending on the class of one or more of its arguments
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ActiVis Class Diagram
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Object–oriented Programming in R
S3 System• Easiest to use• Not fully object–oriented
S4 System• Fully object–oriented• Less computationally efficient when compared to the S3 system
R.oo Package• Extends the S3 system• Easy to use, and more user friendly• Makes use of reference variables
R5 System• Reference Classes• Similar to R.oo, but it is part of R• We decided to use the R5 approach
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Case Study: Single Patient R Code
library(ActiVis)### inialize actigraphy object for this patientpatient <- ActData$new()patient$fileName <- "Patient.AWC"### read the datapatient$read()### create a new raw data plot object for this patientpatient_raw <- RawDataPlot$new()patient_raw$legendPosition <- "topright"patient_raw$act_range <- c(0, 4000)### setup the graph by calling the graphics devicepatient_raw$setup()### show the average of data for the dayspatient_raw$average <- TRUE### what days to plot?patient_raw$plotdays <- c(2, 3)### store this patient’s object in "patient" field in the graph objectpatient_raw$patient <- patient### show the data on the raw data plotpatient_raw$showData()
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Case Study: Raw Data Plot
Patient Raw Data Plot
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Jürgen Symanzik — March 12, 2013 ENAR 2013 Spring Meeting, Orlando, Florida 27
Introduction ActiVis Feature 1 ActiVis Feature 2 ActiVis Feature 3 Conclusion & Future Work Appendix
Feature 3: User–friendly Web Interface
Main users of the R package are doctors in the medical fieldEasy–to–use interfaces are needed (users unlikely to learn R)Approaches:
• Windows interface• Web interface (everyone knows how to operate a web browser)
I Rpad approachI Rook approachI rApache approach
Jürgen Symanzik — March 12, 2013 ENAR 2013 Spring Meeting, Orlando, Florida 29
Introduction ActiVis Feature 1 ActiVis Feature 2 ActiVis Feature 3 Conclusion & Future Work Appendix
ActiVis Web Interface
Jürgen Symanzik — March 12, 2013 ENAR 2013 Spring Meeting, Orlando, Florida 30
Introduction ActiVis Feature 1 ActiVis Feature 2 ActiVis Feature 3 Conclusion & Future Work Appendix
Client–Server Architecture (1)
Jürgen Symanzik — March 12, 2013 ENAR 2013 Spring Meeting, Orlando, Florida 31
Introduction ActiVis Feature 1 ActiVis Feature 2 ActiVis Feature 3 Conclusion & Future Work Appendix
ActiVis Client–Server Architecture (2)
Jürgen Symanzik — March 12, 2013 ENAR 2013 Spring Meeting, Orlando, Florida 32
Introduction ActiVis Feature 1 ActiVis Feature 2 ActiVis Feature 3 Conclusion & Future Work Appendix
Summary
Development of ActiVis R package (Prototype), including
• New visualization tools for actigraphy data
• OO–design
• Web–based user interface
Tested on single data set (55 patients) and on simulated data set
Jürgen Symanzik — March 12, 2013 ENAR 2013 Spring Meeting, Orlando, Florida 34
Introduction ActiVis Feature 1 ActiVis Feature 2 ActiVis Feature 3 Conclusion & Future Work Appendix
Future Work
Finalize R package (and documentation) and submit to CRAN
Test with additional data sets
It’s OO: Extend to different types of actigraphy & functional data!
Extend graphics, e.g., support dendograms in multivariate timeseries plots
More interactivity in the web interface (zooming in/out, more plotparameters, etc.)
Jürgen Symanzik — March 12, 2013 ENAR 2013 Spring Meeting, Orlando, Florida 35
Introduction ActiVis Feature 1 ActiVis Feature 2 ActiVis Feature 3 Conclusion & Future Work Appendix
Further Reading
Interface, 2010Sharif, A., Symanzik, J. and Shannon, W. D. (2010), AnObject–Oriented Approach in R for the Visualization of FunctionalActigraphy Data, Computing Science and Statistics.
Chance, 2011Ding, J., Symanzik, J., Sharif, A., Wang, J., Duntley, S. and Shannon,W. D. (2011), Powerful Actigraphy Data Through FunctionalRepresentation, Chance 24(1), 30–36.
JSM, 2012Sharif, A. and Symanzik, J. (2012), Graphical Representation ofClustered Functional Actigraphy Data, in 2012 JSM Proceedings,American Statistical Association, Alexandria, VA.
Jürgen Symanzik — March 12, 2013 ENAR 2013 Spring Meeting, Orlando, Florida 36
Introduction ActiVis Feature 1 ActiVis Feature 2 ActiVis Feature 3 Conclusion & Future Work Appendix
Web Interface: Main Page
Jürgen Symanzik — March 12, 2013 ENAR 2013 Spring Meeting, Orlando, Florida 38
Introduction ActiVis Feature 1 ActiVis Feature 2 ActiVis Feature 3 Conclusion & Future Work Appendix
Web Interface: Upload Files
Jürgen Symanzik — March 12, 2013 ENAR 2013 Spring Meeting, Orlando, Florida 39
Introduction ActiVis Feature 1 ActiVis Feature 2 ActiVis Feature 3 Conclusion & Future Work Appendix
Web Interface: Files Selected
Jürgen Symanzik — March 12, 2013 ENAR 2013 Spring Meeting, Orlando, Florida 40
Introduction ActiVis Feature 1 ActiVis Feature 2 ActiVis Feature 3 Conclusion & Future Work Appendix
Web Interface: Choose Plot Type
Jürgen Symanzik — March 12, 2013 ENAR 2013 Spring Meeting, Orlando, Florida 41
Introduction ActiVis Feature 1 ActiVis Feature 2 ActiVis Feature 3 Conclusion & Future Work Appendix
Web Interface: Enter some Parameters
Jürgen Symanzik — March 12, 2013 ENAR 2013 Spring Meeting, Orlando, Florida 42
Introduction ActiVis Feature 1 ActiVis Feature 2 ActiVis Feature 3 Conclusion & Future Work Appendix
Web Interface: Plotting
Jürgen Symanzik — March 12, 2013 ENAR 2013 Spring Meeting, Orlando, Florida 43
Introduction ActiVis Feature 1 ActiVis Feature 2 ActiVis Feature 3 Conclusion & Future Work Appendix
Web Interface: The Plot
Jürgen Symanzik — March 12, 2013 ENAR 2013 Spring Meeting, Orlando, Florida 44