A QUESTION-ANSWER BASED HEURISTIC APPROACH TO …

April 7, 2016 15:59 WSPC/INSTRUCTION FILE ws-ijseke

International Journal of Software Engineering and Knowledge Engineeringc© World Scientific Publishing Company

A QUESTION-ANSWER BASED HEURISTIC APPROACH TO

CONSTRUCT DUCG KNOWLEDGE BASE FOR CLINICAL

INTELLIGENT DISEASE DIAGNOSES

SHICHAO GENG

School of Computer Science and Engineering, Beihang University, 37 XueYuan Rd.Beijing,100191,China

[email protected]

QIN ZHANG

School of Computer Science and Engineering, Beihang University, 37 XueYuan Rd.Beijing,100191,China

[email protected]

Received (Day Month Year)

Revised (Day Month Year)Accepted (Day Month Year)

Clinical intelligent diagnostic decision support systems have been a research hotspotfor a long time. As one of the alternative models, Dynamic Uncertain Causality Graph

(DUCG) has been newly presented and applied diseases such as vertigo, jaundice, etc. So

far the results are perfect. However, to successfully apply DUCG in real practice, how tocorrectly model the clinical experts’ knowledge with the DUCG language is critical. In

order to clinical experts can translate their medical knowledge into the DUCG knowledge

base easily, we propose a question-answer based heuristic approach that can elicit clinicalexperts to dig out the causal relationships between diseases and symptoms, diseases and

complications, and complications and symptoms respectively, and then obtain all the

influences between various diseases and symptoms. As a case study, the knowledge baseabout “gastrointestinal-thyroid” disease is elicited with the approach, and is verified in

our experiment. The feasibility and rationality of this heuristic approach are illustrated,which shows that this approach is effective and convenient for use.

Keywords: Heuristic; knowledge representation; clinical decision support system; causal-ity.

1. Introduction

The applications of artificial intelligence in medicine, especially the clinical diag-

nostic decision support systems based on various artificial intelligence technologies,

have been the research hotspot for decades [1] [2].The main task of them is to i-

dentify patients’ possible diseases according to patients’ physical signs, symptoms,

and health history, as well as imaging information and medical laboratory tests.

With the help of intelligent systems, clinical doctors can improve their accuracy of

diagnoses and reduce missed diagnoses and misdiagnoses [3].

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2 Authors’ Names

Rule-based reasoning method, such as MYCIN and INTERNIST-1 [4], etc., are

the earlier systems applied to clinical diagnoses and have been used widely until

now [5].Case-based reasoning medical expert system began to be researched and

applied in the 1980s, and has developed rapidly [6].Many successful medical appli-

cations, such as psychiatry and epidemiology, etc., have emerged one after anoth-

er [7] [8].Since fuzzy logic can be used to handle inaccurate and uncertain medical

knowledge, it has also been widely applied to medical expert system [9].Similarly,

neural network is also well applied in the medical diagnoses [10]. Many other in-

telligent systems have been applied to the diagnoses of skin disease, myocardial

infarction, dyspepsia, gastritis and cancer, etc., as well as for treatment decision-

s [11]. In these intelligent systems, Bayesian network is well known as a probabilistic

graphical model with solid theoretical foundation, and therefore has been used more

frequently in various disease diagnoses such as cancer, heart disease, lumbago and

infectious disease [12].

As a new model, DUCG is newly presented for casual knowledge representa-

tion and probabilistic reasoning [13] [14] [15] [16] [17]. It can represent complex

causal relationships among various event variables intuitively and explicitly, which

is coincident with the knowledge structure of domain experts and therefore easy to

be abstracted form and understood by domain experts. It’s probabilistic inference

is effective and efficient, and the results are explanatory, the latter is important

for real applications. DUCG can also cope with dynamic cases, logic gates capa-

ble of representing any logical relations, uncertain evidence, incomplete knowledge

representations and directed cyclic graphs (DCGs). In clinical diagnoses, DUCG

can work with multiple in which usually one dominates, and bear spurious symp-

toms. DUCG has been initially applied to the diagnosis of vertigo. The diagnostic

accordance rate reaches 88.3% [18].

DUCG-based clinical diagnosis system and many other existing systems are

knowledge-based systems, and one of their key issues is the knowledge acquisition

and the construction of knowledge base by the specific model. Difficulties in knowl-

edge acquisition and construction of knowledge base are the following: (1) Many

doctors have never systematically organized their experience and knowledge, they

can use their experience and intuition to make the right diagnosis, but it is difficult

to describe the diagnostic process. (2) Medical knowledge is complex and uncertain,

so knowledge is easy to lose the symptoms, signs and other variables in the process

of construction. And with the expansion of knowledge base, the doctor is easy to

forget the variables and causal relationships that have established. (3) Doctors can-

not spend a lot of time to learn a highly abstract reasoning model. And reasoning

and expression method of model cannot be fully consistent with doctors diagnostic

way.

Therefore, in the application process of clinical diagnoses of DUCG, how to

easily and systematically translate the expert knowledge in medical domain to the

knowledge base of DUCG model is the primary task of the DUCG-based clinical


Instructions for Typing Manuscripts (Paper’s Title) 3

diagnosis system. This paper presents a question-answer based heuristic approach

to elicit domain experts to construct clinical DUCG knowledge bases. First, based

on the characteristic of medical knowledge and DUCG, medical variables are di-

vided into five categories: “diseases“, “symptom“, “complication“, “pathogenic ab-

normality“, and “risk factor“. Then three heuristic procedures of question-answer

are proposed. “Procedure 1“ elicits a direct causal relationship between “diseases“

and “complications“, “diseases“ and “symptoms“. “Procedure 2“ elicits “compli-

cations“ and “symptoms“ that are caused by “complications“ of “Procedure 1“,

and builds the causal relationship between them. “Procedure 3“ finds “pathogenic

abnormalities“ of “diseases“ and “risk factors“.

Section 2 provides a brief introduction to the concept of DUCG. Section 3

introduces the medical concepts in terms of DUCG, and proposes the question-

answer based heuristic approach to construct the clinical diagnosis knowledge base

of DUCG. Section 4 elicits the knowledge base about “gastrointestinal-thyroid“

disease by the use of the question-answer based heuristic approach. Constructed

knowledge base is verified evaluated by the reasoning of DUCG. Section 5 sums up

this paper and outlines the future work.

2. A Brief Introduction to DUCG

DUCG is composed of a set of nodes/variables and directed arcs connecting nodes.

All the directed arcs represent certain or uncertain causalities among nodes [13], in

which the bidirectional arcs (two directed arcs in opposite directions) and directed

cyclic graphs (DCGs) are allowed [16]. Each node is an event variable with dis-

crete states predefined. The continuous variables can be fuzzily classified as fuzzy

discrete variables and treated as ordinary discrete variables equally [17].Five type

event variables are shown in Table 1: (1) The B-type variable drawn as square rep-

resents root causes without any input. Each B-type variable has a prior probability

distribution; (2) The X -type variable (process variable) drawn as circle represents

the consequence and intermediate causes. They must have at least one input and

may or may not have output; (3) The D-type variable drawn as pentagon is as-

sociated with its corresponding X -type variable, representing the default cause of

the X -type variable. In the case that all other parent variable are absent, the D-

type variable decides the probability distribution of the X -type variable; (4) The

G-type variable drawn as gate represents logic gate, in which any logic relations

among input nodes can be specified; (5) The BX -type variable drawn as double

circle represents integrated causes and has at least one input [19].

In addition to these variables, the directed arcs are also represented as event

variables. In this paper, the directed arcs are denoted as A-type event variable

associated with a weighting factor (rn;i/rn), in which An,k;i,j denotes the virtual

causal event that parent event Vi,j , V ∈ {B,X,D,G},causes the consequence event

Xn,k in which the first subscript indexes the variable and the second subscript

indexes the state of the variable,rn;i > 0 represents the causal relationship intensity


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between Xn and Vi, rn ≡∑

i rn;i. It is obvious that An,k;i,j is a member of the event

matrix An;i. The directed arc can be written in text as Fn;i ≡ (rn;i/rn)An;i.

The dashed directed arc means conditional Fn;i with condition event Zn;i.

When Zn;i is met, the dashed directed arc becomes solid (becomes ), otherwise

it is eliminated. “;” divides the subscripts of child node (former) and parent node

(later). “,” divides the node index (former) and node state (later), “,” can omit

without confusing.

A simple example of DUCG that is the knowledge sub-graph of a disease is

shown in Fig. 1. This sub-graph includes all types of variables used in medicine.In

this paper, the specific medical meanings of the DUCG variables are defined in

Table 1.

Fig. 1. The DUCG knowledge sub-graph of a disease. The example includes DUCG various

graphical representation used in medicine.

3. The Question-Answer Based Heuristic Approach

3.1. The Medical Concepts Related to DUCG

To elicit doctors to find medical variables and causal relationships among them, we

need to design a standard medical term based heuristic approach, so that doctors

can add corresponding variables and causal relationships according to the relevant

problems. In order the related medical variables to find more conveniently, we clas-

sify the medical variables as five kinds: diseases, symptoms (e.g. physical signs and



Table 1. Medical meaning of variable types used in the medical DUCG.

Variable type Medical meaning Symbol

BRoot causes of disease, its prior probability represents

the disease prevalence in the population to see doctors

BX

The integrated posterior probability of disease weighted

by a combination of disease incidence and risk factors.

Because, in clinical diagnosis, the doctor should not only judge

the patient’s disease by symptoms, signs,

and related checks but also need to consider the impact of

risk factors on the disease

XSymptoms, physical signs, complications,

laboratory test, imaging information, and risk factors

D Default causes of risk factors

F

Causal relations (causal impact of disease risk factors on disease,

impact of disease on symptoms and medical tests,

and impact of disease on its complications). The starting

from parent to child

Conditional F F -type causal functional event or event matrix with a condition Z

the various physiological values of human body systems), risk factors, complications

or functional abnormality variables, pathogenic abnormality.

Five kinds of medical variables describe as follows:

“Disease” is an event within the patient body, which is the cause of symptoms,

complications and functional abnormalities. “Disease” can lead to a harmful change

of body in vital function, as well as a change in symptoms, physical signs, and human

body systems. Doctors can make a definite diagnosis of patients’ diseases according

to symptoms, physical signs, imaging test and laboratory test, and then treat the

diseases.

“Symptom” can perceptible or observable and is the result of diseases. There are

multiple forms of symptoms, some of which are subjective feelings, such as dizziness,

chest pain and abdominal distension, etc.; some are not subjective feeling, and need

to examined objectively before being identified, such as mucous membrane bleeding

and urine color deepening, etc.; some can be not only felt subjectively, but also

detected objectively, such as jaundice and fever; some other manifest as changes

in human body system, such as frequent micturition, bulimia and obesity, etc. The

physical sign is known as a vital sign, which is also a phenomenon caused by diseases

and can be detected by physical examination, such as blood pressure disorder, ar-

rhythmia, hepatomegaly and splenomegaly, etc. The vital sign can be used to judge

the state of illness and the level of severity. It’s necessary to determine whether

the various physiological indicators of the human body are abnormal through lab-

oratory test or imaging test, such as blood test, B-ultrasonic test, and CT scan,


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etc.

“Risk factors” are known as the factors that can influence the results of the dis-

eases. The occurrence of risk factors will affect the posterior probability of diseases.

In DUCG, the posterior probability of diseases is equal to the weighted synthe-

sis of the diseased posterior probability and the risk factors. Risk factors have a

great relationship with chronic diseases and infectious diseases. Risk factors are

generally featured by uncertainty, variability and non-specificity; there may be a

very great difference among the risk factors that endanger different patients. In the

paper, individual behavior and lifestyle, personal medical history, family history,

epidemiological factor and occupational factor, which can produce an impact on

the incidence rate of diseases, are called risk factors by a joint name.

“Complication” means that a disease leads to another disease in the develop-

ment process, which is the result of the secondary reaction of disease. For instance,

when blood glucose rises in the long time, diabetics’ kidneys, heart, brain, eyes

and peripheral nerves will be imperiled, as a result of which diabetic nephropathy

and diabetic foot will be caused. The functional abnormality means that diseases

lead to an abnormal change in organ function or hormone secretion, so that organs

cannot work normally. In the paper, complications and functional abnormality are

expressed as a kind of variables, and are not regarded as a disease to be solved.

“Pathogenic abnormality” means the viral infection and pathogenic infection

that can directly cause diseases. The disease incidence has a very large relation-

ship with the pathogenic abnormality. If a pathogenic abnormality occurs, disease

incidence rate will increase greatly. Moreover, pathogenic abnormality produces im-

pacts only on diseases, and cannot impact on symptoms, signs, and physiological

indexes. Virus infection can serve as the main cause of disease incidence, which is

common in infectious diseases, and laboratory examination can serve to confirm

whether virus infection occurs.

For easy expression, upper case letters are used to denote them, i.e., “D”, “S”,

“R”, “C” and “P” respectively. In DUCG model, V ∈ {B,BX,X,G,D} is used

to represent parent event/variables.For the medical use, we attach “D”, “S”, “R”,

“C” and “P” as a subscript of {V} to assign specific meaning to {V}. The detailed

meanings of every kind variable shows below:

BD:In the construction process of DUCG medical knowledge base, we use BD

to represent the set of all diseases. The prior probability is the disease incidence in

the population.

BXD:The integrated causal variable BXD is the posterior probability of dis-

ease as a set of final results. Since the lack of prior probability for an integrated

causal variable, we use root cause variable BD as the basic input of diseases. The

probability of root cause variable’s impact on diseases is equal to “1”. Meanwhile,

the input of such variables as risk factors is also available for BXD that is the inte-

grated causal variable of diseases. The posterior probability of diseases synthesizes

the incidence disease probability, risk factors and pathogenic anomaly of diseases.

XS :It represents the symptom sets that are directly caused by disease BXD in



DUCG medical knowledge base.

XC :It is complications set of disease-causing, and can cause the corresponding

symptoms that is expressed with XCS . Meanwhile, complications or functional

abnormality can cause new complications, expressed with XCC .

XR: It is risk factors that can affect diseases, symptoms, physical signs, physi-

ological indexes and pathogenic abnormality, etc.

XP : It represents pathogenic abnormality set. Risk factors can affect pathogenic

abnormality.

For the causal relationship between these variables types, it can be expressed

in Fig. 2, Fig. 3 and Fig. 4 that uses the form of DUCG graph. The occurrence of

diseases can cause symptoms XS or complications XC . Direct causal relationship

graph between diseases and symptoms or complications is shown in Fig. 2. The

development of complications XC may further cause new complications XCC . And

complications XC and XCC can cause the relational complication symptoms XCS .

In Fig. 2, after joining complications XC , XCC and complication symptoms XCS ,

Fig. 2 becomes Fig. 3.

Fig. 3 has expressed the propagation process after the person has the disease,

and shown causal relationship between diseases and its downstream variables. The

occurrence of disease may be due to effect of pathogenic abnormalities XP , and risk

factors XR can affect diseases BD, complications XC , XCC and pathogenic ab-

normalities XP , thus the posterior probability of the disease is affected. Pathogenic

abnormalities and risk factors belong to upstream variables of diseases, after adding

them to DUCG graph, Fig. 3 becomes Fig. 4.

If the presence of pathogenic abnormalities in Fig. 4, then risk factors that affect

pathogenic abnormalities must exist. Risk factors are the topmost X -type variable

and have no input variables, therefore, need to add a default cause variable DR

that can affect risk factors XR.

In the eliciting process of heuristic question-answer based approach, not all med-

ical variables should be involved. Doctors need only to choose those they concerned

in their medical knowledge base. The following variable types can be exclusive: XP ,

XC , XCC , and XCS . The heuristic question-answer based approach is designed to

help doctors choose the relevant variables conveniently to construct DUCG knowl-

edge bases. Doctors can select the corresponding medical variables as needed and

represent the causal relationship between them.

3.2. Three Specific Procedures of Question-Answer Based

Heuristic Approach

Considering a variable, when focusing on its own, we can only pay attention to it’s

direct upstream or downstream variables, and all find out them. The basic idea

of heuristic question-answer approach to constructing DUCG knowledge base for

clinical diagnoses is through each relevant variable, and exhaustively look upstream

and downstream variables of variable of interest. As can be seen in three figures of


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Fig. 2. The basic DUCG graph that only includes BD, BXD, XS and XC . In the graph,

the direct causality of diseases and complications, diseases and symptoms are expressed. It alsoexpress the causality of BD and BXD .

Fig. 3. The basic DUCG graph that includes VCC and VCS on the basis of Fig. 2.

The graph increases the causality of complications and complications, and all complications andrelated symptoms on the basis of Fig. 2. Disease and its downstream variables are all contained

in the graph.

Fig. 2, Fig. 3 and Fig. 4, DUCG graph is gradually generating a large scale on a small

scale. Doctors can first find out the relationship between diseases and symptoms,

diseases and complications, and then can seek complications and symptoms caused

by complications. Finally, the pathogenic abnormalities and risk factors are added

to DUCG graph.

Considering the DUCG structure of Fig. 2, Fig. 3 and Fig. 4, the paper designs

three heuristic question-answer procedures to guide doctor build DUCG knowledge

base of clinical disease diagnoses. Each procedure elicits overall DUCG structure



Fig. 4. The DUCG graph that includes all types of variables. Pathogenic abnormalitiesand risk factors are added to the DUCG, and establish the related causality. The graph includes

all types of variables about DUCG. The graph can clearly express the causal relationship between

the all types of variables in the DUCG.

graph that respectively are shown in Fig. 2, Fig. 3 and Fig. 4. The heuristic question-

answer method to build the DUCG clinical knowledge base is stepwise refinement

method, at the same time, doctor can control the size of the clinical knowledge

base according to need. The method can start to build a very large-scale knowledge

base from a symptom, or build a small-scale knowledge base to fit use of a specific

problem.

Clinical medical diagnosis is made to identify diseases through symptoms, so

doctors can set a symptom of concern at the beginning and then look for the

related diseases with the heuristic question-answer based approach composed of

three procedures. The main thought of Procedure 1 is to start with a symptom

to find out the diseases related to the symptom, then seeks for the symptoms and

complications related to the diseases, and then search for more diseases in the

symptoms and complications, to iteratively find out all the symptoms, complications

and diseases that doctors regard important. In all procedures, the numerical symbols

on the front represent the step, and in the detailed example, we cite the symbols

on the front to represent the step.

Procedure 1: Represent a direct causal relationship between diseases and

symptoms, as well as between diseases and complications.

Input:the symptoms, laboratory test items or complications selected by doctors.

Output:including the sub-DUCG of the direct causal relationship between dis-

eases and symptoms, as well as between diseases and complications.

The guidance step of Procedure 1 is shown in Fig. 5.

Procedure 1 starts with the major symptoms of a kind of diseases. The corre-

sponding diseases are find by the major symptoms. Then It starts to find out the

direct symptoms of the diseases, and the diseases according to the symptoms. If the


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Fig. 5. The guidance step of Procedure 1. The overall structure of DUCG that is shownin Fig. 2 may be guided by the procedure. During the elicited process, user can find the relatedvariables and causality based on these questions



steps are repeated, all the diseases, symptoms and complications directly caused

by diseases, which doctors consider necessary, can be found out. The basic DUCG

graph that includes diseases, symptoms and complications as well as the causali-

ty among them, is elicited through Procedure 1. The general framework of the

DUCG knowledge graph is shown in Fig. 2.

In Procedure 1, nine key questions-answers are designed, whose steps are num-

bered as 〈02〉, 〈04〉, 〈05〉, 〈09〉, 〈12〉, 〈13〉, 〈16〉, 〈19〉 and 〈22〉. A new disease is elicited

from the initial symptom in step 〈02〉. Doctors are instructed to build a sub-graph of

disease named in step 〈04〉 and 〈05〉. New symptoms, complications are elicited from

disease in step 〈09〉 and 〈13〉. New diseases are elicited from newly added symptoms

in step 〈19〉 and 〈22〉. A causal relationship between diseases and symptoms, as well

as diseases and complications, is built in step 〈12〉 and 〈16〉. Elicited by the nine key

steps, new diseases and symptoms, as well as a causal relationship between them,

are built.

Procedure 2: Add the symptoms or complications caused by complications to

each initially generated sub-graph. Start with the complication variable set to find

out all the complications, and final symptoms.

Input:Elicit the DUCG sub-graph which contains the direct causal relationship

between diseases and symptoms, as well as diseases and complications;

Output:The DUCG sub-graph that contains all the relationships chosen by

doctors between diseases and symptoms, diseases and complications, complications

and complications , as well as complications and symptoms.


The complications that are caused by complications can be added to sub-graph

by Procedure 2. Moreover, the final symptoms caused by complications can be

elicited. Procedure 2 forms a DUCG sub-graph for the causal relationship be-

tween diseases and symptoms, diseases and complications, complications and com-

plications, as well as complications and symptoms. The DUCG structure graph is

as shown in Fig. 3.

In Procedure 2 there are only six key problems, which are 〈02〉, 〈05〉, 〈09〉, 〈12〉,〈18〉and 〈22〉. The new complications XCC caused by complications XC are found

from the complications directly caused by diseases in step 〈02〉. All the complications

caused by XCC are found in an iterative mode in step 〈09〉. The symptoms, physical

signs and relevant examinations caused by all the complications are found in step

〈18〉. The causal relationship between complications and complications, as well as

complications and symptoms, is mainly directed in such three steps as 〈05〉, 〈12〉and 〈22〉.

Procedure 3: Add pathogenic abnormality and risk factors to DUCG knowl-

edge graph. The procedure forms a complete DUCG knowledge graph that contains

diseases, symptoms, physical signs, relevant tests, risk factors and the corresponding

causal relationships.

Input:The DUCG knowledge graph that contains diseases, symptoms, compli-

cations, relevant tests and the causal relationships among them;


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Fig. 6. The guidance step of Procedure 2. The overall structure of DUCG that is shown in

Fig. 3 may be guided by the procedure. Procedure 2 can guide the all downstream variables ofdiseases, these variables include symptoms, signs, complications and related checks, and so on.



Output:The DUCG knowledge graph that contains diseases, symptoms, func-

tional abnormality, laboratory tests, pathogenic abnormalities and hazards, as well

as the causal relationships among them.


Procedure 3 elicits the pathogenic abnormalities and risk factors, as well as

the causal relationships among them. And meanwhile elicits the causal relationships

between pathogenic abnormalities and diseases, risk factors and disease, risk factors

and symptoms, complications, and laboratory tests. A general DUCG knowledge

graph is elicited after Procedure 3 ends.

In Procedure 3, the key steps include 〈03〉, 〈04〉, 〈07〉, 〈10〉, 〈15〉, 〈16〉, 〈19〉, 〈22〉and 〈26〉. All the pathogenic abnormalities of diseases are elicited in step 〈03〉. The

risk factors that cause pathogenic abnormalities are searched in step 〈07〉, and all

the risk factors of diseases are elicited in step 〈15〉. The causal relationships between

risk factors and symptoms, complications are elicited in step 〈19〉 and 〈22〉, in which

there isn’t new variable, and only new causal relationships are elicited. The new

causal relationships are elicited after new pathogenic abnormalities or risk factors

are added in step 〈04〉, 〈10〉 and 〈16〉. The risk factors that do not input are added

to the default causal variables and their causal relationships in step 〈26〉.The causal functional event F with condition Z isn’t involved in the above

Procedure of heuristic question-answer process for the time being, because in the

construction of knowledge base, the design of condition needs to be carried out

according to the established variables, and that some conditions can’t be made

clear at one blow, which need to be set in accordance with the quantity of diseases

and the difference among diseases.For example, in terms of two diseases that are

“alcoholic liver disease” and “nonalcoholic fatty liver disease”, whether the patient

have a history of alcoholism or alcohol which can distinguish two diseases.When the

patient does not a history of alcoholism or alcohol, that can diagnose “nonalcoholic

fatty liver disease” based on related symptoms, and in the case of the patient

has history of alcoholism or alcohol which diagnoses “alcoholic liver disease”. This

judgment is distinguished by conditional causal functional event between diseases

and symptoms, relational checks. The conditional causal functional event can be

set after completing construction of variables.

4. The Application of Question-Answer Based Heuristic Approach

4.1. The Application of Three Procedures

To verify the feasibility of Procedure 1, Procedure 2 and Procedure 3, we need

to use examples to judge whether the effective structure of DUCG knowledge graph

can be elicited through these Procedures. In the following application, we’ll elicit

DUCG knowledge graph from the beginning of such a symptom as “tachycardia”.

In eliciting process, the state of such X -type variables as symptom, physical sign,

complication, relevant examination and risk factors is ignored. In the process of

heuristic question-answer process, whether diseases and complications can cause


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Fig. 7. The guidance step of Procedure 3. The overall structure of DUCG that is shown inFig. 4 may be guided by the procedure. After the end of the Procedure 3, all medical variablesand causality what users care can be elicited. The medical knowledge base that can be used is

established.



symptoms means whether they can cause an abnormal state in symptoms. If a

variable has many abnormal states, a causal relationship can be built as long as one

abnormal state is aroused.

Knowledge is elicited from the beginning of a symptom “tachycardia”. The ba-

sic DUCG knowledge graph of four diseases is elicited through the application of

Procedure 1, as shown in Fig. 5. The graph contains the direct causal relation-

ship between diseases and symptoms, complications. The key steps in Procedure

1 have been applied for 32 times. And 4 diseases, 9 symptoms, and 3 complications,

as well as their causal relationships, are elicited.

Starting from symptom “tachycardia” (X1), step 〈02〉 can elicit the disease “thy-

roid disease” (BX1). Step 〈05〉 can establish a sub-graph G1 and elicit the root

cause B1 of BX1. Step 〈09〉 can find symptoms “palpitation” (X2), “high level of

FT3” (X3), and “high level of FT4” (X4) by disease BX1. Step 〈12〉 can build

the causal relationship between BX1 and X2, X3, X4. Step 〈13〉 can obtain “atri-

al fibrillation”(X5) by BX1. Step 〈16〉 establishes the causal relationship between

BX1 and X5. Thus Fig. 8(a) is established.

Step 〈22〉 can elicit the disease “hiatus hernia” (BX2) from the beginning X5.

Step 〈05〉 can establish a sub-graph G2 that is named “hiatus hernia” and elicit

the root cause B2 of BX2. Step 〈09〉 can find symptoms “barium swallow evidence

of hiatal hernia” (X6), “upper GI endoscopy evidence of hiatal hernia” (X7), and

“burning pain” (X8) by disease BX2. The causal relationship between BX2 and

X6, X7, X8 are built with step 〈12〉. The complication “gastroesophageal reflux”

(X9) can be added to G2 by step 〈13〉. Step 〈16〉 builds the causal relationship

between BX2 and X9. Through these steps Fig. 8(b) is established.

The disease “gastric ulcer” (BX3) can be elicited by X8 according to step 〈19〉.Sub-graph G3 is built and the root cause B3 of BX3 is added to G3 by step 〈05〉.BX3 can elicit the symptom “upper GI endoscopy evidence of ulcer” (X10) by step

〈09〉 and the causal relationship between them is built by step 〈12〉. Using step

〈13〉, the complication “gastric mucosal lesion” (X11) of BX3 is elicited. The causal

relationship between BX3 and X10 is completed. Fig. 8(c) is established by these

steps.

Step 〈22〉 can find the disease “gastritis” by X11. Sub-graph G4 is built and

the root cause B4 of BX4 is added to G4 by step 〈05〉. Using step 〈09〉 can find

the symptom “Upper GI endoscopy evidence of gastritis”(X12) from BX4 and step

〈12〉 build the causal relationship between BX4 and X12. New complications are

not found with step 〈13〉. New diseases have not been added by 〈19〉 and 〈22〉. So

Fig. 8(d) is the last a sub-graph that is elicited by Procedure 1.

The knowledge graph of four diseases is built by Procedure 1. The paper is just

to illustrate the process of eliciting, thereby controlling the size of the knowledge

base. In practical applications, doctor can create an appropriate scale knowledge

base according to the need.

Three complications X5, X9 and X11 are found according to Procedure 1. Pro-

cedure 2 is to find the complications caused by three complications and symptoms


16 Authors’ Names

Fig. 8. The basic DUCG knowledge sub-graph elicited from Procedure 1 in the example

of “gastrointestinal-thyroid”. Starting from the symptom “tachycardia”, four diseases are find,

and symptoms and complications are directly caused by them are also added into sub-graph. Thecausality between diseases and symptoms, complications are find .

of all the complications. Step 〈02〉 can find X5 cannot cause new complications, but

X9 and X11 can cause new complication “stomach disorder” (X13). X13 is added

to sub-graph G2, G3 and G4 that include X9 or X11 by step 〈04〉, and the causal

relationship between them is built by step 〈05〉. X13 cannot cause any complications

by step 〈09〉, so the knowledge final include four complications: X5, X9, X11, and

X13. Using step 〈18〉 can find X5 can cause those symptoms: “tachycardia”(X1),

“palpitation”(X2), “irregular pulse”(X14) and “ECG evidence”(X15). The sub-

graph G1 and G2 that include X5 are added variables X1, X2, X14 and X15, and the

causal relational between them are built by step 〈21〉 and 〈22〉. Similarly, using step

〈18〉 can seek the symptoms “upper GI endoscopy evidence of esophagitis”(X16),

“24-hour esophageal PH monitoring” (X17), “cough” (X18) that are caused by X9

and the symptoms “acid regurgitation”(X19) and “heartburn”(X20) that are caused

by X13. In sub-graph G2, G3 and G4, the causal relationships are built by step 〈21〉and 〈22〉.

In the application process of Procedure 2, the key steps have been applied for

17 times, and elicited the DUCG knowledge graph that contains all the symptoms,



physical signs, examinations and complications or functional abnormality related

to diseases. G1, G2, G3 and G4 are shown in Fig. 9(a), 9(b), 9(c) and 9(d).

Fig. 9. Based on the result of Procedure 1, the four DUCG knowledge sub-graphselicited from Procedure 2 in the example of “gastrointestinal-thyroid”. On the basis ofProcedure 1, complications caused by complications, symptoms caused by all complications, and

all causality are added into four sub-graphs by Procedure 2.

During using Procedure 3, step 〈02〉 cannot find pathogenic abnormalities

of four diseases, so do not care the causal relationship between pathogenic ab-

normalities and diseases, risk factors and pathogenic abnormalities. Risk factors

“gender”(X21) and “age”(X22) that can cause BX1 are elicited by step 〈14〉 and

are added into sub-graph G1, at the same time, the causal relationship between

X21, X22 and BX1 are built. For BX2 of sub-graph G2, step 〈14〉 and 〈16〉elicit risk factors “gender”(X21), “age” (X22) and “overweight”(X23) and build


18 Authors’ Names

the causal relationship between them. In the sub-graph G3 and G4, risk factors

“gender”(X21),“age”(X22),“blood pressure”(X24) and “proton pumps inhibitors”

(X25) and the causal relationship between them and BX3, BX4 which can be find

by step 〈14〉 and 〈16〉. Step 〈19〉 can find the risk factor X22 can caused complica-

tions X5 and X9, and the causal relationship between X21 and X5, X9 are added

in sub-graph G1, G2, G3, G4 by step 〈22〉. Step 〈19〉 and 〈22〉 can elicit the effect of

risk factors X22, X23, X24 and X25 on complications and their causal relationship

in each sub-graph. At last, risk factors are added default causal variables DR by

step 〈26〉. After Procedures 3 is completed, four sub-graphs that include diseases,

symptoms, complications, related checks and risk factors are elicited and are shown

in Fig. 10(a), 10(b), 10(c) and 10(d).

Risk factors and the causal relationships between risk factors and diseases are

elicited in Procedure 3. The key steps in Procedure 3 have been used 35 times.

Because it’s believed that the four diseases have not pathogenic abnormality, the

key steps 〈03〉, 〈04〉, 〈07〉 and 〈10〉 aren’t applied. The DUCG knowledge graph to

be built has been finished after Procedure 3 ends. In the application process of

Procedure 1, Procedure 2 and Procedure 3, doctors can control knowledge

quantity according to actual requirements.

We us such a symptom as “tachycardia” and get the sub-DUCG of four diseases

through heuristic catechetical procedures. 153 steps are used in the above examples,

84 of which are key steps. 4 diseases (4 BX -type and B -type variables respectively),

16 symptoms, physical signs and relevant examinations(X -type variables), 4 compli-

cations or functional abnormality (X -type variables), 5 hazards (X -type variables),

5 default causal variables of hazards (D-type variable) derived and 68 causal rela-

tionships (F -type variable) are elicited. The prior probability of diseases can be set

according to the incidence probability of the diseases among people as recorded in

the relevant literatures, so the degree of causal influence should be set by doctors in

accordance with cardinal symptoms and secondary symptoms. After combining the

four knowledge sub-graphs in Fig. 10 together by DUCG Intelligent Diagnosis Sys-

tem, we can get the composite DUCG knowledge graph of “gastrointestinal-thyroid”

disease as shown in Fig. 11. The repetitive variables and causal relationships in the

sub-charts are merged in the composite graph.

Each step of heuristic question-answer method is focused on the direct causal

relationships, to refine and localize problems, so as to reduce the difficulty of think-

ing. This method mainly helps doctors find out the relevant diseases by symptoms,

and find out the symptoms caused by diseases, so as to work out the causal rela-

tionship between them, to finally sort out the structures of the DUCG knowledge

graph of the whole clinical medicine. The elicited medical DUCG knowledge graph

is borderless, and medical experts can extend it infinitely as long as they are willing

to. The complications or functional abnormality in the procedure can be a kind

of symptoms or relevant tests. The classification can help medical experts to use

better stepwise refinement to build DUCG knowledge graphs.

When using heuristic question-answer method, each doctor can pay attention



Fig. 10. Based on the result of Procedure 2, the four DUCG knowledge sub-graphs

elicited from Procedure 3 in the example of “gastrointestinal-thyroid”. On the basis ofProcedure 2, pathogenic abnormalities and risk are added into four sub-graphs by Procedure 3.

After the end of the Procedure 3, corresponding sub-graphs of four disease are built.

only to the diseases with which he is familiar, and several experts can construct

a knowledge base jointly according to the functions of the DUCG software sys-

tem. Doctors can check whether variables are already exist by the variable checking

function offered by software system. About the knowledge of contradiction, the soft-

ware will give doctors tips automatically, and multiple doctors can unify them after

consultation. If the same knowledge repeatedly appears in many sub-graphs, the

software system will combine it with the composite graph, to ensure the uniqueness

of the knowledge in the final DUCG knowledge graph. Heuristic question-answer

method has improved the construction efficiency and quality of DUCG medical

knowledge base based upon the functions offered by software system, thus ensuring

the consistency and integrity of DUCG medical knowledge base.


20 Authors’ Names

Fig. 11. The DUCG knowledge graph of “gastrointestinal-thyroid” disease. Four sub-graphs of Fig. 10 can be combined into the graph. The graph includes .

4.2. The Verification of Elicited Knowledge Base

The evidence information obtained through inquiry and physical examination is

shown as follows:

E′

1 = X1,1; E′

2 = X2,1; E′

3 = X14,1; E′

4 = X18,1; E′

5 = X19,1; E′

6 = X20,1;

E′

7 = X21,2; E′

8 = X24,4; E′

9 = X23,1; E′

10 = X24,1; E′

11 = X25,1.

The status information of other variables in the knowledge base is unknown.

Making an inference and calculation according to the above evidences, and figure

out the integrated causal variables of diseases and the sorting probability: Hiatus

hernia(BX2:34.27%), Gastritis(BX4:31.91%), Thyroid disease(BX1:29.02%) and

Peptic ulcer (BX3:4.80%). The inferential knowledge graphs of the four diseases

are shown in Fig. 12. In these graphs, light blue represents abnormal symptoms

and physical signs, while light yellow represents abnormal risk factors.

In Fig. 12, there are 3 symptoms that can be explained with B1, 6 symptoms

that can be explained with B2 and only 2 symptoms that can be explained with B3

and B4. The symptoms that can’t be explained with B1, B3 and B4 are affected

by risk factors. Among the four initial events, B2 has the lowest prior probability

(3.3%), but it can explain the largest number of symptoms and that there are three

risk factors which have impacts on BX2, so BX2 has a high posterior probability

and sorting probability. The prior probability of B4 is highest (80%), and that there



Fig. 12. The reasoning knowledge graphs of four diseases that contain the first group

of evidences. In these graphs, light blue represents abnormal symptoms and physical signs andlight yellow represents abnormal risk factors. Systems that can be affected by diseases are clearly

expressed. The effect of risk factors for each disease also can find.

are also three risk factors with impacts on BX4, and the posterior probability of

BX4 is high as well. The prior probability (18%) of B1 is higher than that (11%)

of B3, and that B1 can explain many symptoms, so the integrated causal posterior

probability of BX1 is higher than that of BX3. B3 and B4 have the same causal

chain for symptoms, but the probability of the impact of B3 and B4 on X11 is set

as 0.6, so there is the same causal influence on the posterior probability of B3 and

B4. However, in the calculation of the overall sorting probability, since the effect

on probability isn’t very obvious, but mainly because there is a small difference

among causal influences in degree, and there are causal influential events in the

numerator and denominator of the computational formula of posterior probability,

so the function of causal influence is further weakened. This is also the reason why

relative degree is available to causal influence probability.

Further examine of patient can get further evidence, as shown below:

E′

12 = X6,1; E′

13 = X7,1; E′

14 = X3,0; E′

15 = X4,0; E′

16 = X10,0;E′

17 = X12,0.

All other states with unclear variables are unknown. “Hiatus hernia disease“ can

be confirmed after reasoning. The inferential knowledge graph of the four diseases


22 Authors’ Names

under all evidences is shown in Fig. 13. In Fig. 13(b), the sub-graph of “hiatus

hernia” explains all the symptoms and relevant examinations. But B1, B3 and B4

can’t explain abnormal variable X6 and X7 in Fig. 13(a), 13(c) and 13(d), so

virtual default reasons are used for interpretation.

Fig. 13. The DUCG reasoning knowledge graph under all evidences. In these graph, only

“Hiatus hernia” can explain all the evidence, so the posterior probability of “Hiatus hernia” is

the largest. The reasoning knowledge graph clearly explains the relationship between diseases andsymptoms, risk factors and diseases.

In this example, the result of the relevant diseases was obtained through two-step

reasoning, and the reasoning process and result of the diseases were well explained.

The rationality of the knowledge base built in this paper was verified, which shows

that the elicited knowledge base fits in with such a characteristic of DUCG mod-

el structure and probabilistic reasoning as comprehensiveness and uniformity in

reasoning process.



5. Conclusion

Knowledge acquisition is a difficult and key point of knowledge-based intelligent

system. For DUCG-based clinical diagnostic decision system, the transformation of

medical knowledge into the causal relationship of DUCG model decides the accura-

cy and reliability of diagnosis. Based on the features of DUCG model and medical

knowledge, this paper starts with a symptom and elicited DUCG causal relation-

ship among medical knowledge by heuristic question-answer method that has three

procedures. Procedure 1 elicits the relevant diseases according to symptoms, and

then elicits symptoms according to diseases. After repetitions, it elicits the direc-

t causal relationships between diseases and symptoms, as well as between diseases

and complications. Procedure 2 starts with complications and elicits the complica-

tions and symptoms caused by complications, and built a causal relationship among

them. Procedure 3 elicits the risk factors of diseases, as well as the impact of risk

factors on complications and symptoms. All the relevant medical knowledge can be

elicited through the three Procedures. To verify the Procedures, we started with

“tachycardia” and elicited a simple knowledge graph for “gastrointestinal-thyroid”

disease. The example verified the validity and reliability of the elicited knowledge

base according to the ultimate reasoning and calculation. The heuristic construction

method of DCUG medical knowledge base proposed in this paper can help medi-

cal experts construct a DUCG medical knowledge base quickly and systematically.

With the aid of the functions of software system, it can improve the construction

efficiency of medical knowledge bases and the usability of DUCG clinical diagnostic

decision system.

Acknowledgments

This research is supported by the National Natural Science Foundation of China

under grant 61273330.

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