Download Intelligent Technologies and Methodologies for Medical Knowledge

,
Intelligent Technologies and Methodologies for
Medical Knowledge Engineering
Abdel - Badeeh Mohamed Salem
Professor of Computer Science,
Head of Medical Informatics and Knowledge Engineering Research Unit,
Faculty of Computer & Information Sciences
Ain Shams University, Cairo, EGYPT.
E-mail : [email protected]
http://net.shams.edu.eg/27.htm
10th Annual Med‐e‐Tel conference and exhibition, Luxembourg on 18‐20 April 2012. Goal
• This talk presents some of the intelligent
techniques and methodologies for managing
and engineering medical knowledge.
• The talk covers the following techniques:
(a) case‐based reasoning;
(b) data mining with rough sets; and
(c) ontological engineering .
• Some applications at the BioMedical Informatics
and Knowledge Engineering Research Unit
(BMIKE) at Ain Shams University are discussed
as well.
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Agenda
•
General Introduction
•
Knowledge Engineering Methodologies and Techniques
•
•
•
Case‐Based Reasoning (CBR)Methodology
•
Intelligent Data Mining with Rough Sets
•
Ontological Engineering Approach
Some Applications at MIKE Research Unit at Ain Shams University.
•
Expert Systems for Heart and Cancer Diseases Diagnosis
•
Mining patient data to determine thrombosis.
•
Case‐Based Reasoning for Diagnosis of Cancer Diseases.
•
Web‐Based Breast Cancer Ontology.
Conclusion
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Intelligent Medical knowledge‐based Systems
Intelligent software that performs diagnosis and makes therapy recommendations
Ability of inference, reasoning, perception, learning, knowledge based
Medical knowledge‐based Systems
Intelligent Technologies and Methodologies (Neural Net, Genetic Algorithms, Fuzzy Logic, Rough Sets, CBR)
AI Technology (virtual reality, cognitive science. Nuero sciences, biology, computer science, psychology, philosophy, linguistics, engineering, mathematics)
Computer Aided Decision Support Tools for young physicians
Health care, medical education, Nursing, …
4
Knowledge Engineering
KE deals with the development of intelligent information systems in which knowledge and reasoning play pivotal role.
Knowledge Acquisition
Knowledge Representation
Knowledge management
Knowledge Discovery
Knowledge Engineering Methodologies
Knowledge Modelling
Knowledge-Based Systems
Knowledge Engineering Shells and tools
Knowledge Compilation
Automated Reasoning
Case Based Reasoning
Non Monotonic Reasoning
Temporal Reasoning
Qualitative Reasoning
Reasoning under Uncertainty
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Knowledge Representation Techniques
Knowledge = Static Knowledge + Dynamic Knowledge
Static Knowledge
Dynamic Knowledge
(Heuristics Knowledge )
An Expert is one who knows more and more about less and less. (Micholas M. Butler) Alexandria Library
(April 2002)
Knowledge is Power
An Expert is a person who does not have to think, he knows. (Frank L. Wright))
One who now enough to tell others how to … but is too smart to try it himself. (Henry C. Byce)
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Knowledge Engineering Methodologies
Traditional IF‐THEN Rules
IF the patient has high fever
AND the patient has headache
AND the patient has stiff neck
THEN treat immediately for meningitis
Rules‐of‐Thumb
An apple a day keeps the doctor away Fuzzy Rules
IF THEN
you’re old,
you have owned several homes .
Rules with certainty factors
IF
the patient has hay fever, CF = 0.6
THEN prescribe an antihistamine
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Knowledge Engineering Methodologies
1. Case-Based Reasoning Methodology, CBR
• CBR means reasoning from experiences
or “old cases” in an effort to: solve problems, critique solutions, explain anomalous situations
• CBR is an analogical reasoning method provides both:
– A methodology for building knowledge‐based systems – A cognitive model for People.
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CBR Methodology Case Retrieval & Adaptation Mechanisms Input Problem
Specification
(New Case)
Problem Analysis & Retrieval
Case
Comparison & Matching
Case‐Memory
Solution
New Case
• One of the primary goals of CBR systems is to find the most similar, or most
relevant, cases for new input problems.
• New generations of CBR based knowledge systems:
‐ Uses the CBR methodology as an inference technique.
‐ Uses an extensive past cases as a knowledge structure
‐ Solves new problems by adapting solutions of similar problems.
• The effectiveness of CBRS depend on the quality and quantity of cases in a case
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memory.
Knowledge Engineering Tasks in CBR Systems
In order to implement a CBRS, we had to provide answers to the
following set of questions (adapted from Slade, 1991)
•What constitutes a case and what are its attributes ?
• How is it represented ?
• How is indexing done ? What are the case similarity metrics ?
• What is the retrieval strategy ? What constitutes a relevant case ?
• How can old solutions be adapted ? What are the modification
rules ?
• How does memory change over time ?
Kolodner J., Case Based Reasoning, Morgan Kaufman, 1993.
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Data Mining and Knowledge Discovery Technology
Knowledge discovery in Databases (KDD) process and Data Mining (DM) aim to extract useful
information and discover some hidden patterns and new rules from huge amount of databases, which
statistical approaches cannot discover.
Statistics
Database Technology
AI
Technology
Data
Mining
Machine
Learning
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Producing Knowledge with Intelligent Data
mining approach
Interpreting Mined Patterns Creating a target data set
Removing noise and handling missing data fields
Data Reduction and Finding useful features
Tasks and techniques
Acting on the Discovered Knowledge
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The Data Mining Tasks
DM is the process of using historical databases to improve subsequent decision making.
Maps (classifies) a data item into One of several predefined classes
Seeks to identify a finite set of categories (Clusters) to describe the data
Classification Clustering
Sequence Modeling sequential data
Analysis
Finding a compact description for a subset of data
Summarization
Data Mining Tasks
Regression Link Analysis
Models
Determination of Originate from standard regression analysis; construct a linear or nonlinear function
relationships (dependencies) between fields in a database
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Data Mining Techniques
Support Vector Machines
Neural Networks
Fuzzy Logic
Data Mining Techniques
Case Based Learning
Rough Sets
Genetic Algorithms
Association rule mining
Decision Trees
Popular Data Mining Algorithms and Techniques
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Data Mining Task
Data Mining Algorithm & Technique
Classification
Neural Networks
Support Vector Machine
Decision Trees
Genetic Algorithms
Rule induction
Clustering
K‐means
Regression and prediction
Support Vector Machine
Decision Trees
Rule induction, NN
Association and Link Analysis (finding correlation between items in a dataset)
Association Rule Mining
Summarization
Multivariate Visualization
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Ontological Engineering
• The term “ontology” is inherited from philosophy, in
which it is a branch of metaphysics concerned with the
nature of being.
• In AI, ontology can be defined as a common
vocabulary for describing a domain that can be used
by humans as well as computer applications.
• Ontologies have a range of potential benefits, e.g,
–
–
–
–
Sharing of information across information systems.
Enabling reuse of knowledge and management.
Robust knowledge representation technique.
Providing intelligent and personalized researcher support.
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Types of Ontologies
Describe very general concepts, e.g. space, time, event,
which are independent of a particular problem or domain.
Describe the
vocabulary
related to a
generic task
or activity
(Biology,
(diagnosing,
medicine,
selling,
eductaion,
teaching,
sports).
football).
Describe concepts depending on a particular domain and task.
(It is a particular knowledge base)
Describe the
vocabulary
related to a
generic domain
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Technical Aspects of Ontological Engineering
1.
Methodologies for the design and evaluation of ontologies.
2.
Development of tools to support ontology design and evaluation.
3.
Development and integration of new ontologies.
4.
Ontology Validation
5.
Ontology evolution ( compare versions of ontologies ).
6.
Ontology Mapping and Merging.
Abdel‐Badeeh M.Salem, Marco Alfonse. “Ontological Engineering in Medicine”. Medical Informatics Workshop, ACM Third International Conference on Intelligent Computing and Information Systems, Cairo, Egypt, PP 59‐74, 2007.
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Medical Informatics and Knowledge Engineering
Research Unit
Computer Science Department
Faculty of Computer and Information Sciences
Ain-Shams University
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Research Directions at BioMedical Informatics and Knowledge Engineering Research Unit
Brain
(Tumor Diagnosis)
Dr. Safaa
(Tumor Detection, MRI)
Heba
(EEG Biometrics, Identification)
Wael
Heart
(Expert System, Diagnosis)
Dr. Bassant, Dr. Rania
(ECG Biometrics, Identification)
Manal
Liver Cancer (Ontology)
Marco
Viral Hepatitis
(Ontology)
Galal
Lung Cancer
(Ontology)
Marco
Breast Cancer
(Ontology )
Marco
(Expert System)
Dr. Bassant
Thrombosis
(Data Mining, Rough Set)
Dr. Abeer, Dr. Safia
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Case Study 1: Case-Based Expert System for
Diagnosis of Cancer Diseases
• Cancer is a group of more than 200
different diseases.
•
Cancer occurs when cells become
abnormal and keep dividing
and
forming either benign or malignant
tumors.
• Cancer has initial signs or symptoms if
any is observed, the patient should
perform complete blood count and other
clinical examinations.
• To specify cancer type, patient need to
perform special lab-tests.
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Knowledge Acquisition and Representation
Cancer
Symptoms
Bleeding
Indigestion
Lump
Hoarseness
Types
Clinical examinations
Imaging
…
X-rays
Lump
...
Lung cancer
Urine tests
Liver cancer
Radionuclide
Blood Tests
Staging
Jaundice
Liver symptoms
Weakness
Blood vomiting
Encephalopathy
…
Tests
Direct
Bilibrubin
Treatments
…
SI
S3
S2
…
Liver
Transplantation
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Total Bilibrubin
SGPT
S4
Example of liver cancer case of old Egyptian women:
Patient: 65-years old, female, not working, with nausea and
vomiting.
Medical history: cancer head of pancreas
Physical Exam: tender hepatomgaly liver, large amount of
inflammatory about 3 liters, multiple liver pyogenic
abcesses and large pancreatic head mass.
Laboratory findings: total bilrubin 1.3 mg/dl, direct bilrubin
0.4 mg/dl, sgot (ast) 28 IU/L. sgpt (alt) 26 IU/L.
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Architecture of the hybrid ES for cancer diagnosis
User Interface
Cairo Cancer
Institute, CCI
Querying:
Case symptoms
Lab tests analysis
CBR Model
Case
Memory
Case Retrieval
similarity
matches
Output:
Inference
Diagnostic case
RB Model
Rule based
inference
System Benefits:
1- Aids the young physicians to check their diagnosis
2- Can be used as learning tool to increase the knowledge capabilities for medical students.
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Case Study 2: ES for Diagnosis of Heart Diseases
Stable Angina Pectoris
Unstable angina pectoris
Acute Myocardial Infarction
Subacute bacterial Endocarditis
Right Sided Heart Failure
Silent Myocardial Infarction
Acute Rheumatic Fever
Pericarditis with Effusion
Aortic Incompetence
Pulmonary Hypertension
Types of Heart Diseases
Heart disease is a vital health care problem affecting millions of people.
Essential Hypertension
Secondary Hypertension
Viral Pericarditis
Restrictive Cardiomyopath
Dilated Cardiomyopathy
Audtry Pericarditis
Pulmonary Embolism
Constrictive Pericarditis
Adhesive Pericarditis
Mitral Incompetence
Hypertrophic Cardiomyopathy
Aortic Stenosis
Left Sided Heart Failure
Mitral Stenosis
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1- Rules–Based Version
• The knowledge was gathered
from expert doctors in:
EL-Maadii Military Egyptian
Hospital,
User Interface
Egyptian Health Insurance
Institute,
The Inference Engine:
Rule-Based Reasoning
Medical Books and
Resources.
• The system has been tested for
13 real experiments (patients).
• The results have shown 77 %
Knowledge Base
24 facts
65 Rules for
24 Heart diseases
accuracy in estimating the right
conclusion.
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2- Case-Based Version:
• The knowledge is represented in the form of frames.
• Case-memory contains knowledge for 4 heart diseases namely:
- mitral stenosis
- left-sided heart failure
- stable angina pectoris - essential hypertension.
Case Attributes
Case 1
Case 2
Case 3
Case 4…..
Age
62
41
69
67
Sex
M
F
M
M
Coughexertional
√
√
√
Dyspnea
√
√
√
Palpitation
√
√
√
.
.
.
.
Coughpulmonary
oedema
√
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System’s Technical features:
• For case retrieval, we have developed two algorithms; nearestneighbor and induction algorithm Implemented in Visual Prolog.
• It has trained set of 42 cases for Egyptian cardiac patients
• Each case contains 33 significant attributes resulted from the
statistical analysis performed to 110 cases.
• The system has been tested for 13 real cases.
• Results have shown 95% accuracy in estimating the correct results
for using nearest neighbor and 54% in case of induction
algorithm.
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System's Benefits:
1. serves as doctor diagnostic assistant
2. support the learning for the undergraduate and postgraduate
young physicians.
3. gives an appropriate diagnosis for the presented symptoms,
signs and investigations done to a cardiac patient with the
corresponding certainty factor.
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Case Study 3: Mining patient data to determine thrombosis
diseases using rough sets
Main advantages of rough set theory
•
Deals with vagueness data and uncertainty.
•
Deals with reasoning from imprecise data.
•
Used to develop a method for discovering relationships in data
•
Provides a powerful foundation to reveal and discover important structures in data
and to classify complex objects.
•
Do not need any preliminary or additional information about data.
•
Concerned with three basics: granularity of knowledge, approximation of sets and
data mining
•
very useful in
banking,
practice, e.g. in medicine, pharmacology, engineering,
financial and market analysis.
Z.Pawlak, “Rough Sets: Theoretical Aspects of Reasoning About Data”, Kluwer, 1991
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The data was made through “Discovery Challenge Competition”, organized as part of the 3rd
European Conference on Principals and Practice of KDD in Prague, 1999 (20 MB).
Some of the Discovered Rules
R1. When ANA_Pattern is equal to “S” or “P” or "S, P” this implies that the
increasing possibility of thrombosis where when it is empty the possibility
decrease.
R2. When Diagnosis=" " this implies the decreasing possibility of suffering from
thrombosis.
R3. When the values of the three measures of coagulation KCT or RVVT equal to
empty or "-" it implies decreasing the possibility of suffering from thrombosis,
but when the values are "+" the possibility increases.
R4. When symptoms ="CNS inpus" or "CNS inpus (headache)", "CNS susp" all
patients have thrombosis of value 2.
R5. When symptoms ="any thing else" all patients have thrombosis of value 1.
R6. When the value of total protein increases it implies the decrease possibility of
thrombosis.
System’s Benefits
•
Helps young physicians to predict the thrombosis disease.
•
Predictive rules to determine thrombosis disease
•
Search for patterns specific/sensitive to thrombosis disease.
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Case Study 4: Using Ontological Engineering for Developing
Web-Based Lung Cancer Ontology
Lung cancer, like all cancers, results from an abnormality in the cell, the body's
basic unit of life.
Abdel‐Badeeh M. Salem, Marco Alfonse, Building Web‐Based Lung Cancer Ontology, The International Journal of Soft Computing Applications, ISSN: 1453‐2277 Issue 2, PP 5‐14, 2008. 32
Developing Steps
1. Organizing and scoping: establishes the objectives and requirements. The scope
defines the boundaries of the ontology.
2. Data collection: the raw data needed for ontology development is acquired.
3. Data analysis: the ontology is extracted from the results of data collection.
1. The objects of interest in the domain are listed, followed by identification of objects
on the boundaries of the ontology.
2. Relations between objects can be identified .
3. Adding instances to the ontology.
4. Initial ontology development: a preliminary
( i.e. classes, relations and properties).
ontology
is
developed
5. Ontology refinement : the initial development is iteratively refined.
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Semantic Net Knowledge Representation of Lung Cancer
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Initial Ontology Development
• In this ontology we have 4 main
superclasses
People which has the subclasses;
male and female.
• Medical
_Interventions
which
has
subclasses;
Treatment, Staging and Diagnosis.
• Disease which has subclass cancer which
has subclass; lung_cancers.
• Disease_attributes which has subclasses;
Causes,Disease_stage, Pathological_category,
Staging_systems and Symptoms.
•
The lung cancer ontology was encoded in
OWL‐DL format using the Protégé‐ OWL
editing environment.
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Conclusion
•
Intelligent technologies and methodologies provide approaches,
techniques, and tools that can help solving diagnostic and prognostic
problems in a variety of medical domains.
•
The development of robust intelligent medical knowledge based systems
is a very difficult and complex process that raises a lot of technological and
research challenges that have to be addressed in an interdisciplinary way.
•
Medical knowledge based systems can benefit from systematic knowledge
engineering and structure using techniques from Artificial Intelligence.
•
The web based of such systems can enhance the online medical diagnosis,
education/ learning/training processes.
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Future Directions
1.
Using Behavioral Biometrics technology to eLearning
user
authentication / identification. This technology is based on the user
data of ECG and EEG bio‐signals (Brain Computer Interface).
2.
Genetic
algorithms
thrombosis disease.
3.
Visualization algorithms for medical data mining
4.
Data Mining Techniques in Gene Expressions.
5.
Intelligent Algorithms for Personal Authentication using Palm Vein.
6.
Visualization algorithms for Orthopedic Surgery.
7.
Intelligent Systems for Detecting Brain Tumors from Brain Magnetic
Reasoning Imaging (MRI).
classifier
for
predicting
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International Conference on Intelligent Computing & Information Systems, ICICIS – Ain Shams, Cairo, Egypt
1. First, June 2002
(67 technical papers – 11 keynote speakers)
Workshop on Artificial Intelligence in Medicine
2. Second , March 2005
(60 technical papers – 14 keynote speakers)
3. Third, March 2007
(77 technical papers – 18 keynote speakers)
Workshop on Medical Informatics
4. Fourth, March 19‐22, 2009
(90 technical papers – 18 keynote speakers)
1st International Workshop on Medical Informatics and e‐Health.
1st Intelligent and Assistive Technologies for People with Disabilities 5. Fifth, June 30‐July 3, 2011
(40 technical papers – 3 keynote speakers)
2nd International Workshop on Medical Informatics and e‐Health.
2nd Intelligent and Assistive Technologies for People with Disabilities
sixth, June 2013
38
Thank You