Introducing Bayesian Network in ... - Abt Associates

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7 Νοε 2013 (πριν από 3 χρόνια και 9 μήνες)

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Introducing Bayesian
Networks in
Neuropsychological
Measures

Presented at 2006 APS Annual Conference

Toshi Yumoto

University of Maryland, Abt Associate

Gregory Anderson

Xtria, Adler School of Professional Psychology

Daisy Wise

University of Maryland

Bayesian Networks

It is very difficult for practitioners to combine this
information objectively


Paul Meehl,
Clinical versus Statistical Prediction (1954)



Bayesian Networks provide an effective way to
combine different sources of information such as:


Information from distinct domains


Demographic (e.g. gender, race/ethnicity) and Clinical
Information (e.g. previous diagnosis, different type of tests)


It is easy to add new information or modify existing
information

Bayesian Network (2)


A Bayes Net is suitable for discrete data


Continuous data needs to be converted into categorical data


Complex models can be divided into several
conditionally
independent

parts


Model estimation is easier


Straightforward to add conditional independent data


Bayesian Network is visual and intuitive


Class assignments are expressed as a proportion (e.g. percent)


Provides a prediction for all parameters based on limited (or no)
information


Allows both subjective and objective evaluation of a network



Microsoft Belief Networks (MSBNx, Kadie, Hovel, Horvitz, 2001)
was used to build a Bayesian Network


Part I


Conversion of continuous scores to discrete
categories by Finite Mixture Approach


Part II


Create Bayesian Network based on hypothesized
model


Estimate conditional probabilities


This research used a Latent Class Model


Part III


Examine and Modify model

Steps in Network Development

Measures and Sample


The ATLAS is a comprehensive assessment for
ADHD containing 7 different sections (Anderson &
Post, 2006). One of the sections is a series of
neuropsychological measures and observations of
performance during testing.


This paper examines three of the major traits of the
neuropsychological measures for ADHD.


Diagnoses of ADHD & LD were gathered from a
parent report.


The sample is around 220 subjects, 8
-
18 years of
age, from across the nation, gathered in the test
field trials.

Creation of Discrete scores


The assumption was made that test scores
are from more than one distribution


A Finite Mixture Model was therefore utilized


Cut scores were established using the
intersection of the distributions


# of mixture distribution = # of cut scores + 1


A discrete score was assigned based on a
person’s mixture distribution characteristic


For more information contact authors.

Finite Mixture Analysis for Trail A

Trail A time: Comparison of Observed and Mixture
Distributions
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
10
30
50
70
90
110
130
150
Time in seconds
Proportion
Observed
One Normal
Two Normal
Three Normal
Trail A time: Two Normal Distributions
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
10
30
50
70
90
110
130
150
Time in Seconds
Probability
Normal
Abnormal
Cut Score

Hypothesized Model

Visual Trace
Latent Class
Memory
Latent Class
Impulsive
/
Error
Latent Class
Second Order
Latent Class
LD
/
ADHD
TAtime
TBtime
DA
TA
DA
Cancel
TAerror
TBerror
CancelO
CancelC
Memory
1
Memory
3
Forward
Backward
SS
Conditionally Independent Given
Second Order Latent Class
Model Specification


Three Distinct Domains


Visual Trace/Sequence


Three Latent Classes


Memory


Three Latent Classes


Impulsive/Error


Two Latent Classes


Second Order Latent Class


Four Latent Classes


Diagnosis (LD/ADHD)


Four manifest categories


Typical, LD, ADHD, and LD/ADHD

Specification of the Bayesian
Network


For each domain conditional probability of
indicator variables are specified given latent
class membership (for that domain).


These probabilities are first specified based
on the assumption that we have no
information.


These are then updated, given information
such as test scores or clinical observations.


Partial Model: Visual Trace Network

No information known

Partial Model: Visual Trace Network

All item scores known


Probability of LD/ADHD state given


Middle Visual Trace level


Middle Memory level


High Impulsive level


SOClass and LD/ADHD states are unobserved


Proportions are expected class states


Probability of LD/ADHD state with Six test results


Trail A time (middle) and error (middle)


Trail B time (middle
-
low) and error (middle)


Word Memory 1 (low) and Word Memory 3 (low)


Other nodes have expected category distribution


Highest probability indicates most likely category


It is easy to combine additional information such as clinical observations and gender
to improve model prediction.


Clinical observation is conditionally independent from other nodes given LD/ADHD
states (i.e. only affect the probability of LD/ADHD node)


Gender has direct effect on LD/ADHD states and Impulsive/Error level, which
indirectly affect second order class states.


This type of information is harder to add later and should be included from the beginning, if
appropriate.

Summary


Bayesian Networks provide an effective way to express and
examine hypothesized models.


The model performance can be compared with precision of
prediction (e.g. LD/ADHD diagnoses in this research).


Any statistical procedures estimating expected scores (i.e.
probability of responses) may be used to build a network.


Bayes Net uses discrete data, therefore latent class model and
latent trait model (with discrete proficiency levels) nicely fit model
development.


Combining additional information is straight forward and relatively
easy


Understanding of conditional independence is the key


Bayes Net estimates expected probability from available
information


Makes best possible diagnosis without complete data

Contacts


Toshi Yumoto


fyumoto@umd.edu


Gregory Anderson


ganderson@xtria.com


Daisy Wise


dawise@umd.edu