Download An Ontology Driven and Bayesian Network Based Cardiovascular

An Ontology Driven and Bayesian Network Based
Cardiovascular Decision Support Framework
Kamran Farooq1, Amir Hussain1, Stephen Leslie2, Chris Eckl3, Calum MacRae4,
and Warner Slack5
1
Department of Computing Science and Mathematics, University of Stirling, FK9 4LA, UK
2
Cardiac Unit, Raigmore Hospital, Inverness, IV2 3UJ, UK
3
Sitekit Labs, Isle of Skye, Inverness, IV51 9HL, UK
4
Brigham and Women's Hospital, Cardiovascular Division, Boston MA 02115, US
5
Beth Israel Deaconess Medical Center, Harvard Medical School, Boston MA 02446, US
{kfa,ahu}@cs.stir.ac.uk, [email protected],
[email protected], [email protected],
[email protected]
Abstract. Clinical risk assessment of chronic illnesses in the cardiovascular
domain is quite a challenging and complex task which entails the utilization of
standardized clinical practice guidelines and documentation procedures to
ensure clinical governance, efficient and consistent care for patients. In this
paper, we present a cardiovascular decision support framework based on key
ontology engineering principles and a Bayesian Network. The primary
objective of this demarcation is to separate domain knowledge (clinical expert’s
knowledge and clinical practice guidelines) from probabilistic information.
Using ontologies is a cost effective and pragmatic solution to implementing a
shift from simple patient interviewing systems to more intelligent systems in
primary and secondary care. The key components of the proposed
cardiovascular decision support framework have been developed using an
ontology driven approach. We have also utilized a Bayesian Network (BN)
approach for modelling clinical uncertainty in the Electronic Healthcare
Records (EHRs). The cardiovascular decision support framework has been
validated using a sample of real patients’ data acquired from the Raigmore
Hospital’s RACPC (Rapid Access Chest Pain Clinic). A variable elimination
algorithm has been used to implement the BN Inference and clinical validation
of the “Coronary Angiography” treatment has been carried out using Electronic
Healthcare Records.
Keywords: cardiovascular decision support system, ontology driven decision
support with uncertainty modeling, clinical decision support and Bayesian
Network.
1
Introduction
Coronary heart disease (CHD) is the most common cause of death in the UK, and the
death rate in the UK is still higher than many European countries. Approximately 2
H. Zhang et al. (Eds.): BICS 2012, LNAI 7366, pp. 31–41, 2012.
© Springer-Verlag Berlin Heidelberg 2012
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K. Farooq et al.
million people are currently living with angina in the UK. [http://www.heartstats.org].
This condition is associated with an annual mortality between 2.8% to 6.6% per
annum [1]. The incidence of angina and acute coronary syndromes has been shown to
vary according to risk factors such as age, gender and ethnicity. Patients who present
with chest pain continue to present a major diagnostic challenge for both primary and
secondary care physicians. This is due, in part, to the low specificity of chest pain as a
symptom of significant coronary artery disease and the danger of misdiagnosis in
patients at risk of major cardiac events. Furthermore, chest pain is a very common
symptom, between 20% and 40% of the general population will experience chest pain
in their lives [2] with up to 1% of visits to a general practitioner due to chest pain [3].
Chest pain is also common presenting complaint in patients attending A and E
accounting for approximately 5% of visits to the emergency department. Furthermore,
up to 40% of emergency hospital admissions are due to chest pain [4].
1.1
Rapid Access Chest Pain Clinic
In 2001, the National Service Framework for Coronary Heart Disease made a
commitment to have 50 rapid access chest pain clinics (RACPC) in England by April
2001 [DOH 2000]. These clinics were designed to allow direct access to cardiology
expertise without the need for accident and emergency assessment or admission to a
medical ward. RACPCs would appear to be reliable and safe in the assessment of
patients with suspected cardiac chest pain [5].
1.2
Bayesian Networks
Bayesian Networks hold an important position in modern clinical decision support
systems. Bayesian networks are being exploited in the modern clinical decision
support systems because of their ability to model causal (or diagnostic) relationships
using a degree of clinical expert’s belief, allowing reasoning under uncertainty [6]. In
the Machine Learning world, there are several sophisticated probabilistic models
which are used to model clinical uncertainty, such as fuzzy-logic, BNs, etc. Generally,
a BN of n variables consists of a DAG (Direct Acyclic Graph) of n nodes and a
number of arcs. Nodes Xi in a DAG corresponds to random variables, and directed
arcs between two nodes represent direct causal or significant relationships among
different variables. The uncertainty of the causal relationship is represented locally by
the CPT (Conditional Probability Table) [6]. The P( X i pa( X i )) is associated with
each node Xi, where pa(Xi) is the parent set of Xi. The conditional independence
assumption and the joint probability distribution of X = (X1; X2;:::; Xn) can be
worked out to define CPTs in the Bayesian network, namely, the chain rule of BN: P
(X) = ∏iP( X i X i pa( X i ) . Using the joint probability distribution, BNs support
probabilistic inference in the joint space. Besides the influence of probabilistic
An Ontology Driven and Bayesian Network
33
reasoning provided by BNs themselves, they are being utilized for the structural
similarity between the DAG of a BN and activity graphs of the CPGs (RACPC
guidelines for the chest pain risk assessment): both of them are directed graphs, and
direct correspondence exists between many nodes and arcs in the two graphs [6].
Moreover, BNs can be used to represent the clinical uncertainty in the clinical
practice guidelines and they also facilitate inference capabilities for the ease of
understanding.
The rest of the paper is organized as follows:
Section 2 reviews the state of the art in the healthcare patients’ information
management systems. Section 3 explains our methodology for the development of
Electronic Healthcare Records (in the form of a patient semantic profile). Section 4
presents preliminary results which includes ontology testing and validation results
using real patients' data acquired from the Raigmore Hospital, this section also
focuses on the clinical uncertainty modeling in the Electronic Healthcare Records
using a novel Bayesian Belief Network and finally some concluding remarks are
given in section 5.
2 Background
This research focuses on the comparative study of traditional patients’ information
management systems based on conventional hard-wired branching logic. These
systems have significant limitations, including: lack of flexibility and adaptability to
complex clinical requirements and processes and a general lack of astuteness [7].
These clinical interviewing systems do not go far beyond core functionalities and due
to their rigid architectures, these conventional systems are hard to maintain and
update [8]. We are developing an ontology driven decision support framework as
shown in Fig 1 to solve such knowledge representation and clinical decision making
issues in the cardiovascular domain.
Fig. 1. Cardiovascular Decision Support Framework for Primary and Secondary Care
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2.1
K. Farooq et al.
Ontology Driven Approach
Ontology engineering is a popular branch of artificial intelligence which is used in the
simulation of complex computing systems, clinical decision support systems,
emotions sensing, sentic computing, opinion mining using language corpuses and
semantic web applications [9, 10]. The study conducted in [11], [12] showed
exceptional results in the risk assessment and disease management of breast cancer
patients which was deployed as a commercial clinical system. They utilized the
semantic web approach to model the clinical practice guidelines which were encoded
in the clinical decision support system for generating patient specific
recommendations. The adoption of Ontologies inspired approach yields good results
in terms of standardizing health care guidelines. The construction of knowledge base
through an ontology inspired approach will have the benefit of problem
independence. This knowledge base can be extended and reuse in a variety of
different problems and therefore will have multiple mapping among knowledge base
and decision models. The knowledge base will update the decision models without
any costly software engineering work and maintenance will be cost effective across
decision models and within the knowledge base.
3
3.1
Methodology
Electronic Healthcare Records
SNOMEDCT has become a de facto gold standard for the modern healthcare systems
because of its easy to use interface with powerful search capabilities in clinical terms.
We have utilized ontology driven approach to model patient’s medical history in
OWL from heterogeneous data sources in legacy clinical systems. We have used a
reverse engineering approach to generate a patient semantic profile ontology using the
patient’s data provided by RACPC (Rapid access chest pain clinic) nurses at
Raigmore Hospital in Inverness. The Patient Semantic Profile is one of the key
components of the proposed decision support framework. The Clinical decision
support framework relies on the data encapsulated in the form of electronic healthcare
records to perform the decision support (using domain specific ontologies) and
dynamic logic operations.
3.2
Ontology Development
The high level design of the classes was carried out with a view to incorporate the
actors/participants which take part during the course of GP referral to RACPC clinics
followed by the cardiologist consultation should they need to be consulted regarding
abnormal ECG or failure to do an exercise tolerance test. They are also referred as
agents in the ontology performing specific actions within the ontology domain. The
ontology design incorporated different stages during the referral process through
relationships among parent-subclasses. The information about patient’s chest pain
type, their past family history, previous cardiovascular history and personal
demographics information is also modelled through domain-specific classes within
the ontology design.
An Ontology Driven and Bayesian Network
3.3
35
Object Properties and Data Properties
The object properties are defined in order to establish relationship between individual
classes. The properties are also referred as Roles or relations in UML terms. The
purpose of the data properties is to be able to define the relationship between
individual class and the XML schema data type.
Object properties establish a relationship between specific classes in order to
encapsulate and model the desired behaviour which is set as a clinical use case.
The modelling of chest pain patients who suffered from suspected angina has been
achieved via the object property “has_chest_pain_type which binds the specific pain
type with the patient using “Patient" and “Chest_Pain_Type” Classes. The XML
schema data type comes from the data properties which describes pain _type as an
enumerated type showing the values as “typical, atypical or nontypical”.
“Has_diagnosis_done” object type describes the relationship between “Patient” and
“General Medical Practitioner”, “Cardiologist” and “RACPC” classes. This
relationship encapsulates and models the behaviour of diagnosis done at each stage by
the clinicians involved in the referral process.
3.4
Ontology Evaluation and Testing
In order to test the developed ontology, several test patients were introduced as part of
the training. The patient semantic profile ontology was used to generate their
electronic healthcare records. The information shown in Fig 2 is a formal
representation of the information initially collected through the test data. After
performing the consistency checking, Pellet dynamic logic reasoner was used to
check the classification results.
3.5
Ontology Testing Results
The patient semantic profile component generated the clinical histories using legacy
patients’ data. Many items of information which are clinically useful to the clinicians
(GPs, Nurses, and Cardiologists) are being held using Boolean-type clauses.
The critical medical conditions are modelled using “ Has Presence and “ Has
Absence” clauses , this sort of clinical information is very useful for clinicians involved
in primary and secondary care and without spending too much time they can get a
bigger picture of the patient’s medical history and diseases/conditions which need
urgent attention from referral perspective. The purpose of this clinical history is to lend
a helping hand to clinicians during clinical decision making and to flag potential clinical
issues which need urgent attention or further examination by clinical experts.
3.6
Important Historical Information
In cardiology clinic the key information which is critical to make effective clinical
decision making is the episodic information pertinent to a heart attack or a heart
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K. Farooq et al.
abnormality of any kind in the past. This critical information is of huge importance in
pre-operative risk assessment before any surgical operation is scheduled for the
patient. This information is modeled through an ontology using “ has absence” and
has Presence” data types.
Qualitative Information.The Qualitative information is presented in the Figure 2 by
example 4. It shows that the patient is suffering from chest pain which is “Typical”
chest pain. What clinically defines typical pain can be asserted in the chest pain risk
assessment ontology developed as part of the chest pain risk assessment ontology.
Cardinal Information. In web ontology language, one of its best features to specify
cardinal information using cardinal restrictions by expressing it in number and ranges.
There are two types of cardinal restrictions you can apply in OWL; they are referred
as Temporal and Quantity units.
Patient’s age is modelled as 75 years in item 4 which personify temporal unit. One
of the best selling features of OWL is the functionality it providers to define unit
classes and storage information in a single unit which can be updated in a cost
effective way when new clinical guidelines are provided by the healthcare
authorities.
Range Information. The range information is modelled in 5 which can make
clinicians aware that the patient in question was diagnosed with coronary angiography
treatment 7 months ago.
Fig. 2. Electronic Healthcare Records in the form of Patient Semantic Profile
4
4.1
Preliminary Results
Ontology Testing Using Real Patients
Web ontology language (OWL) allows ontology engineers to define individuals and
then used them to assert specific properties to test the hierarchical relationship and
An Ontology Driven and Bayesian Network
37
domain logic implemented through various classes. These Individuals (Instances of
the classes) can also be used in class descriptions, namely in “hasValue” restrictions
and enumerated classes. Electronic Healthcare Records have been generated for the
test patients represented in Fig 3. These test patients were inserted as individuals in
the ontology for the training and testing purposes and also to assert specific clinical
conditions which describes their clinical symptoms.
Patient’s medical history encapsulates their demographics along with their past and
present cardiac and non cardiac related clinical symptoms, location of the current
chest pain (left side of the chest) association of the chest pain with breathing, its
severity and whether or not their chest pain is spreading. After analyzing the patient’s
medical history generated through the Patient Semantic Profile ontology the
semantics are extracted to generate electronic healthcare records. These EHRs are of
utmost importance for the clinicians for the effective risk assessment operations and
efficient clinical decision making.
Fig. 3. Chest pain patients' data acquired from the RACPC at the Raigmore Hospital
4.2
Bayesian Network for Uncertainty Modelling in the Electronic Healthcare
Records
We have developed a Bayesian network a model as presented in Fig 4, using the
Electronic Healthcare Records generated in [13] for the clinical risk assessment of
Coronary Angiography treatment, called AngioNet. This model has been created
using patient’s demographics and clinical exam findings data provided by the
consultant cardiologist from the Raigmore Hospital. We have exploited the
probabilistic risk assessment (BN) technique for the validation of clinical scenarios
which are being followed in the RACPC (Nurse-Led Rapid Access Chest Pain
Clinics). The main purpose of implementing this intelligent risk assessment
mechanism is to assess the patient’s suitability for the “Coronary Angiography”
treatment. This treatment is overly prescribed because of misleading/wrong diagnosis
and we aim to help NHS hospitals reduce excessive cost which is being spent on this
overly exhaustive and expensive angiography treatment.
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K. Farooq et al.
Fig. 4. A Bayesian Belief Network for the Coronary angiography risk assessment
4.3
Clinical Validation
A RACPC nurse is trying to perform a risk assessment of a chest patient (Tim James) who has
been referred to this specialized chest pain clinic through a GP referral. Tim is 75 years of age,
suffering from atypical chest pain. He also has a history of CAD, high blood pressure, diabetes
and absence of myocardial infarction. Tim is also a passive smoker.
After loading the semantic profile ontology, the BN inference engine performs
inference using patient’s data encapsulated in the electronic healthcare records as
shown in Fig 5. The patient’s clinical history (risk factors, previous cardiac
conditions) has been presented as observed evidence, such as observations of
hypertensive disorder, smoking, previous CAD and myocardial infarction in this
scenario. If the Nurse selects the target activities, the BN inference engine calculates
the probability of each of these risk factors using the variable elimination algorithm.
RACPC nurse gets a snapshot of the patient’s clinical history and the observed
evidence of historic CAD and previous cardiac treatments. RACPC nurses would like
to know the probability of current activity high BP, Hyper cholesterol, age >75, with
positive ETT”, previous GP, cardiologist and nurse findings to help them ascertain
patient’s suitability for the coronary angiography treatment. In the BN inference
engine, since the activity “ has presence of problem high blood pressure " is found, its
property is Observed is set true and the property hasState is set false. Similarly, the
activities instances of historical myocardial infarction is set to false, occurrence of
smoking is also set to true and observed in the same manner.
The developed AngioNet classifier calculates argmaxy p(y/x) using the distribution
P(U), represented by the Bayesian Network as follows:
p( y / x ) = P(U ) / P( x)
α p(U )
= ∏ p( μ \ pa(μ ) )
μ∈U
(1)
An Ontology Driven and Bayesian Network
39
Since all variables (risk factors) in x are learned through the patients ‟ data, we
applied Bayesian Inference algorithm for all available class values.
Bayesian Inference results have been presented in Figure 5 which shows the
propagated probability distribution for all the variables. BN calculates the probability
of activity instance “absence of myocardial infarction” which is used for the
recommendation of coronary angiography treatment.
Fig. 5. Bayesian Belief Network Inference Results
(
)
P Xq E =
(
P Xq, E
P (E )
) =  X \ {X
q,
X E }P( X )
 X \ X E P( X )
= 0.745
(2)
Where X q = {presence of CAD; recommend “angiography}, and E= {presence of
high blood pressure, problem “myocardial infarction” = false, presence of “smoking”
user finding = true, presence of risk factor “diabetes"= true}. BN provides a degree of
uncertainty keeping in view the presence and absence of key clinical risk factors, a
nurse can choose this target activity instance based on the observed evidence E.
Through BN inference, we can obtain:
P( X q / E =
P( X q , E )
P( E )
) = 0.657
(3)
The results in the two cases have demonstrated high probabilities for the target
activities (risk factors), which can help RACPC nurses in clinical decision making on
the basis of observed evidence presented by the Bayesian Network. The results of this
clinical validation coincide with a clinical expert’s outcome which was initially
carried out using RACPC‘s clinical practice guidelines produced by the National
Institute of Clinical Excellence in the UK. This approach also shows the feasibility of
our approach which ensures effective and accurate clinical decision making by
utilizing Bayesian Network and key ontology driven components, specifically
electronic healthcare records.
40
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K. Farooq et al.
Conclusions
We have presented an ontology driven and Bayesian Network based cardiovascular
decision support framework. We have discussed the development of the key components
which are Adaptive Information collection system using chest pain risk assessment
questionnaire ontology and Patient Semantic Profile for the generation of Electronic
Healthcare Records. We also presented a Bayesian Network for uncertainty modelling
using Electronic Healthcare Records which have been developed as part of the
cardiovascular decision support framework to facilitate accurate and effective clinical
decision making. This approach has paved the way for the automation of RACPC service
which is very much paper based and led by the cardiology nurses in most of the hospitals
in the UK. The proposed system will be able to replace existing RACPC service in major
cardiology clinics in the UK. The main advantages of the ontology driven approach are as
follows: This allows cost effective maintenance of the chest pain decision support system
because of a clear demarcation among knowledge base and decision support functions.
Ontology layer enables the system to perform decision support operations which are hard
to implement using distributed system technologies and centralized databases. This
approach also facilitates the adoption of a generic component based approach for the ease
of reuse and extension of this decision support framework to include other diseases in the
cardiovascular domain and automate conventional nurse-led paper based clinics.
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