Data processing: artificial intelligence – Knowledge processing system
Reexamination Certificate
1999-08-02
2003-07-29
Starks, Jr., Wilbert L. (Department: 2121)
Data processing: artificial intelligence
Knowledge processing system
C600S300000, C704S257000
Reexamination Certificate
active
06601055
ABSTRACT:
BACKGROUND OF THE INVENTION
a. Field of the Invention
The instant invention is directed toward an explanation generation system for a computer-aided decision support tool employing an inference system. More specifically, it relates to a computer-aided diagnosis support tool employing an inference system and an interactive multimodal explanation generation system therefor. The explanation generation system produces interactive multimodal explanations for the results generated by the inference system in the diagnostic support tool.
b. Background Art
A woman has a 1 in 8 chance of developing breast cancer in her lifetime. In 1995, an estimated 183,400 women in the United States were newly diagnosed with breast cancer, and 46,240 died of the disease. Screening mammography effectively detects early breast cancers and can increase the likelihood of cure and long-term survival. Differentiating between benign and malignant mammographic findings, however, is difficult, with approximately 75% of mammograms classified “indeterminate.”
Successful diagnosis depends on the ability of a physician to detect mammographic abnormalities and to integrate clinical information such as risk factors and physical findings to determine the likelihood of breast cancer. Only 15%-30% of biopsies performed on nonpalpable but mammographically suspicious lesions prove malignant. Unnecessary biopsies are costly in terms of physical and emotional discomfort to the patient. Subsequent radiographic abnormalities from biopsies can be mistaken for cancer. Thus, the cost of screening mammography is increased.
Computer technology in the form of a clinical decision-support tool can be employed to improve the diagnostic accuracy and cost-effectiveness of screening mammography. Automated classification of mammographic findings using discriminant analysis and artificial neural networks (ANNs) has already indicated the potential usefulness of computer-aided diagnosis. ANNs learn directly from observations with a knowledge base of impenetrable numerical connection values. Although they perform well, ANNs do not provide for meaningful explanation generation.
Bayesian networks—also called belief networks or causal probabilistic networks—use probability theory as an underpinning for reasoning under uncertainty. One could use Bayesian networks as the formalism to construct a decision support tool. This tool integrated with a clinical database would provide accurate, reliable, and consistent diagnoses. A Bayesian network could perform a differential diagnosis by specifying the observed symptoms and computing the posterior probability of the various diagnoses using standard probability formulas.
Bayesian Networks provide a number of powerful capabilities for representing uncertain knowledge. Their flexible representation allows one to specify dependence and independence of variables in a natural way through a network topology. Because dependencies are expressed qualitatively as links between nodes, one can structure the domain knowledge qualitatively before any numeric probabilities need be assigned. The graphical representation also makes explicit the structure of the domain model: a link indicates a causal relation or known association. The encoding of independencies in the network topology admits the design of efficient procedures for performing computations over the network. A further advantage of the graphical representation is the perspicuity of the resulting domain model. Finally, since Bayesian networks represent uncertainty using standard probability, one can collect the necessary data for the domain model by drawing directly on published statistical studies.
A Bayesian belief network—a graphical representation of probabilistic information—is a directed acyclic graph. The graph is “directed” in that the links between nodes have directionality, that is, they are “one way.” The graph is “acyclic” in that it cannot contain cycles or “feedback” loops. The nodes of the network represent random variables (stochastic)—uncertain quantities—which take on two or more possible values or states. The states of a node define the set of possible values a node can be in at any one time. Each state is associated with a probability value; for each node, these probability values sum to 1. The states for any node are mutually exclusive and completely exhaustive. The directed links signify the existence of direct causal influences or class-property relationships between the connected nodes. The strengths of these nodes are quantified by conditional probabilities. In this formalism, variables are given numerical probability values signifying the degree of belief accorded them, and the values are combined and manipulated according to the rules of standard probability theory.
A Bayesian network contains two types of nodes: nodes with parents and nodes without. A node with at least one parent is represented graphically with a directed link connecting the parent node to the child node. In Bayesian terminology the parent node influences the child node. A node with a set of parents is conditioned on that parent set. A node with no parents is represented graphically with no directed links coming into the node. This latter type of node represents a prior probability assessment and is represented or quantified by an unconditioned prior probability representing prior knowledge.
The strengths of influences between the nodes are represented with conditional-probability matrices associated with the connecting links. For example, if node Z has two parent nodes X and Y, the conditional probability matrix specifies the probabilities of the possible values that Z can assume given all possible combinations of values that X and Y can assume.
The prior and conditional probability values used to build a Bayesian network can be derived directly from published values of sensitivity and specificity and collected from expert opinion.
The primary operation of a Bayesian network is the computation of posterior probabilities. A posterior probability of a variable is the probability distribution for this variable given all its conditioning variables. This inference operation consists of specifying values for observed variables, e.g., setting a node state to one, and computing the posterior probabilities of the remaining variables. The mathematics used in a Bayesian network is described as follows:
Let X be a random variable with n possible states, x
1
, . . . , x
n
. Let Y be a random variable with m possible states, y
1
, . . . , y
m
. The probability of a variable X, P(X), is a real number in the interval 0 to 1. P(X)=1 if and only if the event X is certain.
The probability of any event X being in state x
i
is denoted by
P
(
X=x
i
)=
p,
where
p
is the degree of belief accorded to
X
being in state
x
i
.
The conditional probability of any event X being in state x
i
given a context Y is denoted by
P
(
X=x
i
|Y
)=
p,
where
p
is the degree of belief accorded to
X
given the context
Y.
The joint probability of any events X being in state x
i
and Y being in state y
j
is denoted by
P
(
X=x
i
, Y=y
j
)=
p,
where
p
is the degree of belief accorded to
X=x
i
and
Y=y
j
.
The probability distribution of a node X with possible states x
1
, x
2
, . . . , x
n
, is denoted by
P
(
X
)=(
x
1
, x
2
, . . . , x
n
), given
x
i
≧0 and &Sgr;
x
i
=1, where
x
i
is the probability of
X
being in state
x
i
.
The product rule in probability is denoted by
P
(
X|Y
)·
P
(
Y
)=
P
(
X,Y
). [1]
The probability distribution of X can be calculated from the joint probability distribution, P(X,Y), by summing over the partitions as denoted by
P
(
X
)
=
∑
j
=
1
m
P
(
X
,
Y
)
.
[
2
]
The inversion formula (Bayes Theorem) in probability is denoted by
P
(
Y|X=e
)=
P
(
X=e|Y
)·
P
(
Y
)/
P
(
X=e
), where
e
is user-observed evidence. [3]
A conditional probability di
Dorsey & Whitney LLP
Starks, Jr. Wilbert L.
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