Curriculum of Medical Information Technology Engineering · - Types of wireless networks: Bluetooth, Infrared (IR), Wi-Fi, ZigBee, Mobile Networks, Satellite Networks - Sensor networks

University of Tehran

Faculty of New Sciences and Technologies

Curriculum of

Medical Information Technology

Engineering

Master of Science

General characteristics of the field

M.Sc. in Medical Information Technology

Definition and purpose of the field

The purpose of the field of medical information technology is to use the ICT empowering field to

collect, store and retrieving, sending, processing and representing information in the

Professional-practical field of health and medical. In this regard, students will be specialized in

one of the following fields: telemedicine, health information systems, and decision support

systems in healthcare.

Necessity and importance of the field

Today, the world academic space has turned into interdisciplinary researches. Looking at the

current scientific trends, it can be seen that the production of a new technology or the

presentation of a new theory requires the use of several disciplines and different expertise

together. Under these conditions, professionals who are familiar with the literature of

interdisciplinary research and development and have gained the necessary expertise can help in

velocity, quality and accuracy of researches activities. One of the fields of interdisciplinary work

that has many evidences and applications in the world around us is the subject of information

technology and its applications in various fields.

The spread of information technology has caused irreversible changes in various aspects of

human life over the past years, and the specialized IT-based fields have been based on this. The

area of health and medicine is no exception to this evolution. In order to achieve the long-term

goals of the country and the five-year development plans, the goal is to train well-trained and

efficiently trained, knowledgeable day labor force.

The length of the course and the form of the system

The form of the course system will be thermo-unitary and each theoretical unit is equivalent to

16 hours, and each unit of practice or laboratory is equivalent to 32 hours during a semester. The

total length of the course is 2 years. In order to obtain a master's degree, the student is required to

complete 32 courses.

Number and type of Course units

During this period, 32 courses will be offered to the students in two main and optional courses

along with seminars and theses. The composition of the syllabus for the medical technology

engineering course is described in the following table.

Status of units and field courses.

Number of units Course type

13 Main

9 Optional

6 Thesis

28 Units (Excluding

Compensative Units) Total

The role and ability of graduates

Graduates of this field will have the knowledge and insight in both the field of information

technology empowerment and the field of Professional-practical health and medical. Therefore,

they can be used in all fields of application of information technology in health and medicine

such as hospital information systems, electronic medical content, virtual education and e-

learning, telemedicine, e-health services, decision-making and intelligent medical systems,

electronic health, medical latent systems, GIS with health and medical applications, data transfer

and information in medical projects in interdisciplinary teams with diverse expertise.

Student Admission Requirements

Students can participate in the entrance exam in accordance with the rules and regulations of the

Ministry of Science, Research and Technology.

Materials and coefficients of the test

Rows of the

course group

Exam Courses Coefficient

1 General and Technical language 2

2 common courses (Discrete Mathematics, data structure,

algorithm design, software engineering, computer networks)

4

3 Principles and Fundamentals of Management 0

4 Common Professional Courses (Database Design Basics,

Artificial Intelligence, Operating Systems).

4

Second Chapter

Tables of Courses

Table 1:

The compensative courses of Medical Information Technology Engineering in Master's degree.

prerequisite Number of hours Number of units Name of

Course

row

Total Practical Theoretical Total Practical Theoretical

48 0 48 3 0 3

Introduction to

Algorithms

1

48 0 48 3 0 3

Advanced

Programming

2

96 0 96 6 0 6 Total

Taking these units is upon the advice of the education department.

Table 2:

The main courses of Medical Information Technology Engineering in Master's degree.

prerequisite Number of hours Number of units Name of

Course

row

Total Practical Theoretical Total Practical Theoretical

32 0 32 2 0 2

Medical

Terminology

1

48 0 48 3 0 3

Principles and

Applications

of Information

Technology in

Medicine

2

48 0 48 3 0 3

Application of

intelligent

systems in

medicine

3

48 0 48 3 0 3

Biomedical

Signal

Processing

4

32 0 32 2 0 2 Seminar 5

208 0 208 13 0 13 Total

Passing all 13 units of the above table is required.

Table 3:

The optional courses of Medical Information Technology Engineering in Master's degree.

prerequisite Number of hours Number of units Name of Course ro

w Total Practical Theoretical Total Practical Theoretica

l

48 0 48 3 0 3

Wireless Sensor

Networks in

Healthcare

1

48 0 48 3 0 3

Security and

Privacy in

Healthcare

2

48 0 48 3 0 3

Data Mining and

Knowledge

Management in

Healthcare

Systems

3

48 0 48 3 0 3

Information

Retrieval and

search engines

4

48 0 48 3 0 3

Artificial Neural

Networks 5

48 0 48 3 0 3

Complex Health

Networks 6

48 32 16 3 2 1 Health GIS 7

48 0 48 3 0 3

Advanced

Database 8

48 0 48 3 0 3

Clinical Decision

Support Systems 9

48 0 48 3 0 3 Machine learning 10

48 0 48 3 0 3

Statistical Research

Methods 11

48 0 48 3 0 3 Network Security 12

48 0 48 3 0 3

Health Multimedia

Networks 13

48 0 48 3 0 3

Evolutionary

Computing and

Fuzzy systems

14

48 0 48 3 0 3 Bioinformatics 15

48 0 48 3 0 3

Probabilistic

Graphical Models 16

48 0 48 3 0 3

Healthcare

information

systems

17

48 0 48 3 0 3 Special Topics 18

864 32 832 54 2 52 Total

Passing 9 credits from the table below is required by the supervisor.

Note: The student can, with the discretion of the supervisor, receive a course from the courses

approved for other related subjects as optional courses.

Third Chapter

Syllabuses

Main courses of the field

Medical Information Technology Engineering

Master's degree

Medical Technology

Units: 2 theoretical units Hours: 32 theoretical hours Type: Main Prerequisite: None

Course objectives:

The purpose of this course is to familiarize students with various terminology and concepts in the

field of medicine. Students also get acquainted with various types of medical documents in this

course

Headlines: - Understanding the components of the medical terms (medical, English and Persian)

- Common and unusual anatomical and physiological prefixes

- Internal Diseases

- Surgery

- Children

- Infectious and common symptoms

- Tests, Photographs

- Therapeutic and therapeutic measures

- Medical statistics

Familiarity with the family of diseases and the causes of it

- Full familiarity with international classification systems including DCR-10, ICD-DA, ICD-0, ICD-NA,

CDDG and the like.

- Review of medical records

- Description of outpatient records

- Description of health documents

- Standardization of medical records

- Familiarity with the world's leading medical records standards (definition, field of activity and standards

related to medical records)

- Medical records standards in Iran

Grading

Quiz and Homework Midterm Final Project

10% 45% Written Exam (45%) 0%

Practical Exam (0%)

Resources and references:

• Medical terminology, BarbaraCohen

• Medical terminology, Birmangam

• Medical terminology, Juanita.J. Davies

• Medical terminology, Austerine andClarjan

• Abbreviation, Dorland's

• Abbreviation, Neil.M.Davis

Principles and Applications of Information Technology in Medicine


Course objectives:

The purpose of this course to familiarize students with the concepts and principles of information

technology, including the recognition of productive technology and information technology, the

information society and related concepts, the impact of information technology on various

aspects of the socio-cultural socioeconomic community and the recognition and application of

these systems. The ultimate goal of this course is to familiarize students with the various

applications of information and communication technology in the medical field, which can be

categorized mainly in four sections: the applications of information and communication

technology in telemedicine and the role of telecommunications and networks in this field; Health

information systems and health databases; the application of smart systems in medicine,

including artificial intelligence, data mining, machine learning, and the role of information

technology in policymaking and management in the field of public health and medicine.

Headlines:

Medical terms and medical documents - Understanding the components of the medical terms (medical, English and Persian)

- Common and unusual anatomical and physiological prefixes

- Internal Diseases

- Surgery

- Children

- Infectious and common symptoms

- Tests, Photographs

- Therapeutic and therapeutic measures

- Medical statistics

Familiarity with the family of diseases and the causes of it

- Full familiarity with international classification systems including DCR-10, ICD-DA, ICD-0, ICD-NA,

CDDG and the like.

- Review of medical records

- Description of outpatient records

- Description of health documents

- Standardization of medical records

- Familiarity with the world's leading medical records standards (definition, field of activity and standards

related to medical records)

- Medical records standards in Iran

Principles and Applications of Information Technology in Medicine

- Definitions and concepts of information and communication technology

- Information technology pillars (data, information, knowledge, manpower, hardware, software,

artificial intelligence, infrastructure, etc.)

- Information Systems Architecture and Information Systems

- Localization of software and open standards

- Intellectual Property Rights and Supported Information Technology Laws in Iran

- web 2.0 and its applications in health information technology

- Introduction to the field of medical information technology engineering and its goals

- A desirable and ideal situation in the field of medical information technology

- The current state of the country and the world in the field of medical information technology

- Challenges and challenges ahead

- Understanding the structure and scope of health care (users, patients, doctors, nurses,

managers...)

- The role of telecommunications and telecommunications in medicine and health

- Remote Medicine and its Applications (Telemedicine / Telenursing)

- Types of wireless networks: Bluetooth, Infrared (IR), Wi-Fi, ZigBee, Mobile Networks,

Satellite Networks

- Sensor networks and whole body networks (BAN and WBAN)

- RFID and NFC and their applications in medicine

- Computer application in diagnosis and treatment

- Electronic Patient File (EHR)

- Health Information Systems (HIS)

- Electronic health or E-health

- The role of the Internet in medicine (cybermedicine)

- Transmission or epidemiology of Infodemiology information

- Homemade or Homecare / Telecare Care

- Mobile health or m-health and various new technologies and technologies available to health

and health

- Introduction to information processing technologies in medicine

- Familiarity with technologies and tools for data security and maintaining the confidentiality of

medical information

Grading


10% 25% Written Exam (50%) 15%

Practical Exam (0%)


• Essential Medical Terminology, Juanita Davies

• Health Information management, Edna K. Huffman (last edition)

• Health Information Management Principles and Organization for Health Record Services,

Margaret A. Skurka (last edition )

• B.K. Williams, S. Sawyers, Using Information Technology, Mc Graw Hill, 9th Edition, 2010.

• E. Turban, D. Leinder, E. McLean, J. Wetherbe, Information Technology for Management:

Transforming Organizations in the Digital Economy, Wiley, 5th Edition, 2007.

• N. Davis, M. LaCour, Health Information Technology, Saunders Elsevier, 2007.

• J.T. Marchewka, Information Technology Project Management, Providing Measurable

Organizational value, 3rd edition, Wiley, 2010.

• m. Khansari, h Rabiee, Introduction to Free / Open Source Software, High Council of

Informatics, 2006

• "Engineering System and Software Production and Development Standards (NAMATN)",

the Supreme Informatics Council of the country, 2004B. Fong, A.C.M. Fong, C.K. Li,

Telemedicine Technologies, Information Technologies in Medicine and Telehealth, Wiley,

2011.

• R. Hoyt, Medical Informatics: Practical Guide for Healthcare and Information Technology

Professional, Lulu.com, 2010.

• P. Taylor, From Patient Data to Medical Knowledge - The Principles and Practice of Health

Informatics, Blackwell Scientific, 2006.

• F. Sullivan and J. Wyatt. ABC of Health Informatics, Blackwell BMJ Books, 2006.

• E. H. Shortliffe G. Wiederhold, L. E. Perreault, L. M. Fagan (editors). Medical Informatics:

Computer Applications in Health Care and Biomedicine, (3rd

Edition), Springer Verlag, New

York, 2006.

• J.H. van Bemmel, M.A. Musen (Editors). The Handbook of Medical Informatics, Springer-

Verlag, New York, 1998.

Application of Intelligent Systems in Medicine


Course objectives:

The purpose of this course is to familiarize students with the widespread use of artificial

intelligence (including search-based methods, machine-learning methods, and inference-based

methods) in medicine.

Headlines:

Part one: Introduction to Artificial Intelligence and its Application in Medical Information

Technology

Part two: Search Engine Intelligent Systems

- Graph Search

- Conviction

Part Three: Intelligent Systems Based on Machine Learning

- Introduction to Machine Learning

- Decision tree

- Bias theory

- The closest neighbors

- Linear Separator

- Neural Networks

- Support vector machine (SVM)

- Choosing the characteristic in learning a car

Part four: Intelligent systems on data presentation and inference

- First-order logic and logic

- Resolution

- Logic in action

Grading


30% 35% Written Exam (35%) 0%

Practical Exam (0%)


S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 3rd ed., Upper Saddle

River, NJ: Prentice Hall, 2009

Biomedical Signal Processing


Course objectives:

The purpose of this course is to familiarize students with the types of medical images and

signals, and the various methods for processing these signals and images. In this regard, the

proposed processing methods will include signals and images in two random and non-random

areas.

Headlines:

Part I: Biological Signals and Images

- Introduction of Biological Signals

o Includes: ENG, EMG, ECG, EEG and ...

-Familiar with a variety of medical imaging methods

o Contains: X-ray, CT, PET, MRI, ultrasound and ...

Part II: Principles for processing signals and non-random images

- Received data

- Digital filtering

- DFT and FFT

- Medical signal processing

- Medical image processing including filtering, interpolation, noise reduction, edge

detection and homomorphic filtering

Section 3: Chance and Random Signals

- Introducing the random variables and the probability density function

- Grouping

- Estimation of the probability density function

- Random signals

- Separation of resources blindly

Part IV: Division and registration of medical images

- Share pictures

- Record images

- Analyze and categorize images

Grading


30% 35% Written Exam (35%) 0%

Practical Exam (0%)


R. M. Rangayyan, Biomedical Signal Analysis: A Case-Study Approach, 1st Ed., Piscataway,

NJ: Wiley-IEEE Press, 2001

P. Dhawan, Medical Image Analysis, 2nd ed., Hoboken, NJ: John Wiley and Sons, 2011

R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd ed., Upper Saddle River,

NJ: Prentice Hall, 200

Optional courses of the field

Medical Information Technology Engineering

Master's degree

Wireless Sensor Networks in Healthcare

Units: 3 theoretical units Hours: 48 theoretical hours Type: Optional Prerequisite: None

Course objectives:

The goal of this course is to familiarize students with sensor networks in the fields of medicine,

health and health. Students are familiar with the basics of wireless sensor networks with the

qualities, uses, and considerations of the quality and quantity of such networks in the field of

health.

Headlines:

• Understanding sensor networks

• Applications of sensor networks include various applications in the field of medicine, including

monitoring patient status, tracking patients and doctors in the hospital environment, managing

pharmaceuticals in hospitals, and ...

• Features and Advantages of Wireless Sensor Networks

• Application-based architecture

• Structure and components of sensor networks

• Types of medical sensors

• Principles of Wireless Sensor Networks Design

• Architecture and sensor network topologies

• Energy management

• Wireless network standards and Access protocols

• Study of routing methods in wireless networks

• Security survey on wireless networks

• Reliability in the exchange of information

• Locate wireless sensors-Optimal scatter of sensor nodes

Grading


10% 25% Written Exam (50%) 15%

Practical Exam (0%)


• Jamil, Khan & Mehmet Yuce, Wireless Body Area Networks for Medical application, New

Development in medical engineering, 2006, pp 591-628

• Terrance J. Dishongh, Michael McGrath, Wireless Sensor Networks for Healthcare

Applications, Artech House; 1st Ed., 2009

• Ivan Marsic, Wireless Networks: Local and Ad Hoc Networks, New Jersey: Rutgers

University

• Geoff V Merrett and Yen Kheng Tan, "Wireless Sensor Networks: Application-Centric

Design", Intech Publisher, 2010

• I. Stojmenovic, Handbook of Sensor Networks: Algorithms and Architectures, ISBN:

0471684724, Wiley-Inter Science (2005)

• N. Bulusu and S. Jha, Wireless Sensor Networks: A Systems Perspective, ISBN 1580538673,

Artech House (2005)

• H. Karl and A. Willing, Protocols and Architectures for Wireless Sensor Networks, John

Wiley & Sons, 2005.

• K. Sohraby, D. Minoli, and T. Znati, Wireless Sensor Networks, Technology Protocols, and Application, John Wiley & Sons, 2007.

Security and Privacy in Healthcare


Course objectives:

The purpose of this course is to familiarize students with the concepts and examples of

maintaining security and privacy in the health and medical field, to study examples of the laws of

different countries to preserve the security and privacy of medicine, to examine the technical

requirements of this area, as well as to express the complexities and requirements for

maintaining security. And privacy is in modern medical technology.

Headlines:

• The concept of security and its related definitions

• The concept of privacy

• The need for privacy in medicine, and the problems caused by privacy breaches

• Privacy in the traditional system

o Discussions of medical ethics and patient rights

o Security of paper documents

• Privacy in IT-based medical systems

• Definitions and rules of safety and privacy in medicine

o HIPAA and HITECH laws

o Rules in Iran and other countries

• Topics in data security and network security, password algorithms, and security protocols

o Security policy and access control models

o Network and firewall control

o Digital signature

o Email security

• Anonymity of medical information and medical records anonymity

• Types of medical records and access control models

• Security of medical communications and security of medical images

o HL7 and DICOM standards and security issues

o Security in PACS and Medical Imaging

• Remote medical monitoring systems and portable devices

o Security in remote monitoring

o Security of Medical Records on Portable Devices (Smart Phones)

• Medical identity theft

Grading


10% 25% Written Exam (50%) 15%

Practical Exam (0%)


C. A. Shoniregun, K. Dube, F. Mtenzi, Electronic Healthcare Information Security, Springer,

2010. ISBN: 978-0387848174.

T. L. Hebda, P. Czar, Handbook of Informatics for Nurses and Healthcare Professionals, 5th

Ed., Prentice Hall, 2012. ISBN: 978-0132574952.

K. Beaver, R. Herold, The Practical Guide to HIPAA Privacy and Security Compliance,

Auerbach Publications, 2003. ISBN: 978-0849319532.

S. S. Wu, A Guide to HIPAA Security and the Law, American Bar Association, 2007. ISBN:

9781590317488

M. S. Brodnik, M. C. McCain, L. A. Rinehart-Thompson, R. Reynolds, Fundamentals of

Law for Health informatics and Health information Management, AHIMA, 2009. ISBN:

978-1584261735

Data Mining and Knowledge Management in Healthcare Systems Units: 3 theoretical units Hours: 48 theoretical hours Type: Optional Prerequisite: None

Course objectives:

The purpose of this course is to familiarize students with different methods of data mining and its

applications in the field of medicine.

Headlines:

- Introduction of data mining and its applications in the field of health

- Display knowledge in data mining

- Machine learning in data mining

- Classification methods

- Clustering methods

- Evaluation in data mining

- Combined methods

- Explore the data sequence

- Data mining in text, multimedia and web data

- Prediction and Estimation

- Data mining systems in health

Grading


10% 25% Written Exam (50%) 15%

Practical Exam (0%)


J. Han and M. Kamber , Data Mining: Concepts and Techniques, 2nd ed., Elsevier Inc.,

2006

H. W. Ian and F. Eibe, Data Mining: Practical machine learning tools and techniques,

Morgan Kaufmann, San Francisco, 2005.

Information Retrieval and Search Engines


Course objectives:

Search engines and data retrieval have changed our access to data. Search engines, besides

general search on the Internet, have many uses, including medical search, business search, and

academic search. The purpose of this class is to teach students how to build a search engine. This

class focuses on the basic concepts of search engines, including indexing, prioritizing, creeping,

and evaluating, and will focus on how to decide on the size and type of data to be retrieved and

the requirements of the underlying system. In the advanced discussions of this course, some of

the concepts of machine learning will be used, so students need to be familiar with the subject of

machine learning.

Headlines:

• Boolean recovery

• The term vocabulary & postings lists

• Dictionaries and resilient

• Create profile

• Compress profile

• Scoring, term weighting, and vector space model

• Calculate scores in the complete search system

• Evaluation in data retrieval

• Feedback relevance and query development

• Retrieve XML

• Probabilistic information retrieval

• Language models for data retrieval

• Categorize the text and method of Naive Bayes

• Categorize vector space

• Vector support and machine learning in documents

• Flat HTML clustering

• Hierarchical clustering

• Matrix decompositions & latent semantic indexing

• Basics of Web Search

• Creep on the web and profiles

• Link analysis

Grading


10% 25% Written Exam (50%) 15%

Practical Exam (0%)


Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, “Introduction to

Information Retrieval”, Cambridge University Press. 2008. ISBN: 0521865719

Artificial Neural networks


Course objectives:

The purpose of this course is to familiarize students with the principles of neural networks and its

applications. Students will get acquainted with the theory of neural networks, their capabilities

and constraints, and the way they design and implement them.

Headlines:

• Define neural networks and their differentiation features

• Human neurons and brains, neuron structures

• Overview of natural neural networks, concepts of definitions and their constructive parts

• Processor elements, connections,

• Formation of Patterns

• Advantageous networks

• Single-layer recursive networks

• Two-way communication networks

• Hopfield Network

• Boltzmann Machine

• The medium field theorem

• Learning Models

• Learning with Supervision

• Learning Uncontrolled

• Learning with evaluation

• Self-organizing networks and competitive learning

• Mexican Hemp and Hamming Networks

• The law of learning Kohonen

• Learning Vector Quantum Network

• Layered networks and the law of error propagation back

• Improvement of the Error Release Network and its different versions

• Degree of training and network strength

• Radial base functions networks

• Typical applications

Grading


10% 25% Written Exam (50%) 15%

Practical Exam (0%)


Hecht-R Nielsen, Neurocomputation, Addison Wesley, 1990

L. Fausette, Fundamentals of neural networks, architecture, algorithms and application,

Prentice Hall, 1994.

S, Hykin, neural networks, a comprehensive foundation, Macmillan college pub, 1994.

D.P. Mandic, J.A. Chambers,. Recurrent neural networks for prediction learning algorithms,

architecture and stability, John Wiley & Sons, 2001

M.A. Arbib, the handbook of brain theory and neural networks, MIT press, 2003

Complex Health Networks


Course objectives:

The purpose of this course is to familiarize students with the sciences and technologies of the

network and its applications in the field of medicine, health and health.

Students in this course, while familiarizing themselves with the field of network science and

network characteristics and their importance in our world today, have a profound understanding

of the types of networks in the field of health and their applications. Also, they are familiar with

the methods of network analysis and modeling, and then, using various analytical tools, they

examine some of these networks in the field of health and health.

Headlines:

- Introduction to Network Science

- Types of Health and Medical Networks

- Applications of network science in the field of health

- Measurement of the network and its indicators

- Measuring centrality in networks

- Hierarchical structure and clustering of networks

- Random Walk and Random Networks

- Small-world networks

- scale-free networks

- Evolution of networks

- Search in networks

- Networking

- Consistency and reliability in networks

Cascade behavior in networks

- Publish information on networks

- Epidemic dissemination in networks

- Introduction to Network Analysis Software

Grading


10% 25% Written Exam (50%) 15%

Practical Exam (0%)


• Mark Newman, “Networks, An Introduction”, New York, Oxford University Press Inc.,

2010

• David Easley, Jon Kleinberg, “Networks, Crowds and Markets”, New York, Cambridge

University Press Inc., 2010

• Wouter De Nooy, Andrej Mrvar, Vladimir Batagelj, “Exploratory Network Analysis with

Pajek”, New York, Cambridge University Press Inc., 2005

• Ted G. Lewis, Network Science: Theory and Application, Wiley; 1 edition (March 11,

2009), ISBN: 978-0470331880

• Ernesto Estrada, Maria Fox, Desmond J. Higham, Gian-Luca Oppo, "Network Science:

Complexity in Nature and Technology", Springer, 2010.

• Newman, M., A.-L. Barabasi, et al. (2006). The structure and dynamics of networks,

Princeton University Press.

• Boccaletti, S., V. Latora, et al. (2006). "Complex networks: structure and dynamics."

Physics Reports 424: 175-308.

• Newman, M. E. J. (2003). "The structure and function of complex networks." SIAM Review

45(2): 167-256.

Health GIS Units: 1 theoretical unit and 2 practical units Hours: 16 theoretical hours and 32 practical

hours Type: Optional Prerequisite: None

Course objectives:

The goal of this course is to familiarize students with the principles of the GIS and its

applications in collecting, compiling, updating and analyzing spatial data to identify health

problems and locating these problems with other complications and other factors. Environments

and, ultimately, location-based processing of health data and location-based solutions.

In terms of understanding the distribution and diffusion of diseases and their relationship with

environmental factors (such as climatic conditions, water quality, health status, agricultural and

industrial activities, environmental factors, etc.), GIS can be of great help. Researchers and

valuable tools in the field of etiology (causation) of diseases and health issues. Of course, in all

areas of research on health issues, spatial distribution issues are of great importance, and spatial

information systems are the best tools in this regard.

This course will help students understand some of the basic concepts of how GIS can be used to

track and analyze the geographical distribution of populations at risk, health outcomes, and risk

factors.

Headlines:

Theoretical (1 unit)

- History and introduction to GIS basics and its applications in different fields and sciences

- Introduction of the structure of geographic information systems and its components

- Understanding the structure and data formats in geographic information systems

- Types of image systems and coordinate system

- Understanding Global Positioning System (GPS)

- Geographic data sources and how they are collected in public and health areas

- Models and spatial analysis in GIS

- The vast application of geographic information systems in the health and health system

Practical (2 units)

- Understanding the types of GIS applications and their general applications

- Understanding the ArcGIS application form and exploring and identifying data and information

- How to enter information from various public and health sources

- Working with tables and databases in GIS

- Classifying and displaying health data with GIS

- Designing maps for health studies

- Creating elements and components of the map

- Preparation of spatial data for environmental hazards and spatial analysis

- Adaptation of layers and modeling for location of sanitary facilities

- Perform a practical project

Grading


10% 25% Written Exam (25%) 15%

Practical Exam (25%)


E.K. Cromley, S.L. McLafferty (2002). GIS and Public Health. New York: The Guilford

Press. ISBN: 1-57230-707-2, 340pp. second edition of this textbook is due out in November

2011

L. Lang, GIS for Health Organizations, (Redlands, Calif.: ESRI Press, 2000)

W.L. Gorr. GIS Tutorial for Health by Kristen S. Kurland, 2006. The ESRI Press. Redlands,

CA

A.C. Gatrell, Geographies of Health: An introduction, 2001. Blackwell Publishing. Oxford,

UK.

M.S. Meade, R.J. Earickson, Medical Geography, 2000. The Guilford Press. New York.

Advanced Database


Course objectives:

The goal of this course is to introduce students to advanced concepts in database management

systems, with emphasis on their applications in health and medicine. The course first explores

the basics of the relational database as the most widely used health and medical data center, and

then new and advanced topics are covered in this area. Students in this field have a deep

understanding of the principles of designing and managing the database and its various

infrastructures and architectures with emphasis on health applications. In addition, in this course,

the student will familiarize himself with the application of new ideas in emerging technologies in

the field of health database.

Headlines:

- An overview with the basic concepts of relational database management systems

- Data models and data modeling

- Query optimization and processing

- Transactional processing and concurrency control

- Object-related and object-oriented databases

- XML and semi-structured databases

- Parallel and distributed databases

- Business intelligence technologies: data warehousing and data mining, on-line analytical

processing, data mining,

- Multimedia databases

- NoSQL Database

- Health databases: Diseases coding, tumor, cancer and so on

Grading


10% 25% Written Exam (50%) 15%

Practical Exam (0%)


Elmasri and Navathe, Fundamentals of Database Systems, 6th Ed., Addison Wesley, 2011.

A. Silberschatz, H. F. Korth, S. Sudarshan, Database System Concepts, 6th Ed., McGraw

Hill, 2010.

T. M. Connolly, C. E. Begg, Database Systems: A Practical Approach to Design,

Implementation, and Management, 5th Edition, Addison Wesley Publishing, 2009.

Ullman and Widom, Database Systems: The Complete Book, 2nd Ed., Garcia-Molina,

Pearson, 2008.

M. Kifer, A. Bernstein, P. M. Lewis, Database Systems: An Application Oriented Approach,

2nd Ed., Addison Wesley, 2005.

Clinical Decision Support Systems


Course objectives:

The goal of this course is to familiarize students with the application of decision making in the

field of health and decision support systems in this area. These systems can help diagnose

diseases, prescribe medications, choose better treatments and ... help doctors and clinicians.

In this course, first, decision making and modeling and automated reasoning are discussed, then

the architecture of decision support systems and common examples of these systems are

introduced and its various applications in health systems are introduced.

Headlines:

Application of decision making in the field of health

Modeling the decision

Decision analysis

Uncertainty modeling

Automatic reasoning

Decision-making in the presence of uncertainty

Architecture of decision support systems

Group Decision Making

Principal systems and expert systems

Decision-making systems in the field of health

Grading


10% 25% Written Exam (50%) 15%

Practical Exam (0%)


E. Turban, R., Sharda, D, Delen, Decision Support Systems and Intelligent Systems, 9th

Ed.,

Prentice Hall 2010.

Eta S. Berner, Clinical Decision Support Systems: Theory and Practice, 2nd

ed. Springer,

2010.

Machine Learning


Course objectives:

The purpose of this course is to provide practical knowledge of machine learning to master

students and teach how to use machine learning to solve real problems. Students are expected to

acquire the ability to develop intelligent and intelligent systems in the field of medicine and

health.

This course is an introduction to car learning. Learning is a field of artificial intelligence in

which the computer learns how to solve problems by viewing educational data or through

interaction with its environment.

Today, machine learning is used as the basis of the most important applications of computers.

These include search engines, e-commerce, advisory systems and robotics. This course will

focus on practical learning of the machine. Most of the courses are made up of projects, and

students will build and solve the learning problems of the machine.

Headlines:

• Defining machine learning, examples of its applications and types of learning

• A review of learning methods

• Learning strategies

• Direct inclusion of knowledge

• Learn from the instructions

• Learning with argumentative inference

• Learn through comparison

• Learn from examples

• Learn through observation and discovery

• Kernel methods, backup vector machine

• Constructive induction, various inductive learning techniques, inductive logic programming

• Inference learning techniques

• An explanatory method

• Enhancement learning

Grading


10% 25% Written Exam (50%) 15%

Practical Exam (0%)


Bishop, Pattern Recognition and Machine Learning, 2007

Tom M. Mitchell, Machine Learning, Mc Graw Hill, 1997

G. Paliouras, V. karkaletsis, C. Spyropoulos, Machine learning and its applications, Springer

Verlag, 2001

T. Hastie, R. Tibshirai, J.H. Friedman, The elements of statistical learning, Springer, 2003.

Ethem alpaydin, Introduction to machine learning, MIT press, 2004.

Bernhard schlkopf, Alexander J. Smola, Learning with kernels, support vector machines,

regularization, optimization and beyond, MIT press, 2001.

Christopher M. Bishop, Pattern recognition and machine learning, Springer, 2007.

Nello Christianini, John Shawe-Taylor, An introduction to support vector machines and

other ernel based learning methods, Cambridge University Press, 2000

Stephen Muggleton, Inductive logic programming, Academic Press, 1992.

Statistical Research Methods


Course objectives:

The purpose of this course is to familiarize themselves with the tools and methods of research

and their application for the design and development of a scientific research project. Students

will learn how to design research questions, choose the right research design, collect and analyze

data and how to report the results of the research project. Methods for evaluating projects and

research proposals are among the topics discussed in this course. At the end of this course, the

student is expected to get acquainted with how to design a research project in the field of medical

information technology and identify the research opportunities available in this area through

practical projects.

Headlines:

• Introduction to research methods

• Methods and tools for data collection, interview, questionnaire, observation

• Research methods based on Survey

• Sampling methods

• How to design a survey to achieve research goals

• Sampling plan methods

• How to evaluate a scroll

• Introduction to statistical methods

• Data screening and visualization methods.

• Analysis of one-variable data

- Chi-square methods

- Nonparametric techniques

- T-test & Z-test

• Multivariate data analysis

- log-linear models and agreement tables

- Analysis of variance and covariance

• Survival Analysis

• Data analysis with presence of dependent variable

- Multiple regression

Logistic regression

- How to evaluate a regression model

• Regression models with a large number of variables

- Ridge Regression

- Regression of Las Vegas

- Main component regression

• How to evaluate a statistical model

- Using error criteria like MSE

- Cross-validation, Bootstrap and Jacknife technique

Grading


10% 25% Written Exam (50%) 15%

Practical Exam (0%)


L. Blaxter, C. Hughes, and M. Tight, How to research.Open University Press, 2010.

J. Bethlehem, Applied Survey Methods: A Statistical Perspective, 1st ed. Wiley, 2009.

O. J. Dunn and V. A. Clark, Basic Statistics: A Primer for the Biomedical Sciences, 4th ed.

Wiley, 2009.

Gelman and J. Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, 1st

ed. Cambridge University Press, 2006.

W. E. Saris and I. N. Gallhofer, Design, Evaluation, and Analysis of Questionnaires for

Survey Research, 1st ed. Wiley-Interscience, 2007.

D. Muijs, Doing Quantitative Research in Education with SPSS, Second Edition. SAGE

Publications Ltd, 2010.

T. P. Ryan, Modern Engineering Statistics, 1st ed. Wiley-Interscience, 2007.

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining,

Inference, and Prediction, Second Edition, Springer, 2009.

Network Security


Course objectives:

The purpose of this course is to provide a range of security topics for networked computer

systems. The course addresses the goals of privacy, accuracy and accessibility for computer

networks and provides services that can meet these goals. Network security architectures,

including PKI, and the use of directory services and access control in networks are studied.

Headlines:

• Introduction to networking and computer security

• Security threats, routing attacks, tracking

• Confidentiality of traffic

• Overview of cryptography, PKI security architectures, directory service X.509 and

KERBEROS

• Network access layer security, ATM security services, PPP, PAP, CHAP, EAP, ECP, and

L2TP protocols.

• Internet security layer, closed filters, NAT, IPSec, VPN, firewall and its design principles,

secure systems

• Security Layer Security, SOCKS V5, SASL, ISAKMP

• Application layer security, content filters, authorization and access control, network

communication and security threats and malware (virus, worm, and Trojan horse), e-mail

security, e-mail security, PGP, S / MIME, Web security, SSL, SET, Java security, network

management and SNMP security

• Intruders, Infiltration, Intrusion Detection Services, Intrusion Detection Systems

• Monitoring and RMON

Grading


10% 25% Written Exam (50%) 15%

Practical Exam (0%)


William Stallings, “Network Security Essentials: Application and Standards,” Prentice-Hall,

2000. (4th

edition, 2011, ISBN: 978-0136108054)

S. Ghosh, “Principles of Secure Network Systems Design,” Springer-Verlag, 2002. ISBN:

978-0387952130.

Eric Mainwald, “Network Security: A Beginner's Guide,” Osborne/McGraw-Hill, 2002. (3rd

edition, 2012, ISBN: 978-0071795708)

William Stallings, “Cryptography and Network Security: Principles and Practice,” Prentice-

Hall, 1998. (5th

edition, 2011, ISBN: 978-0136097044)

E. Fisch, G. White, “Secure Computers and Networks,” CRC Press, 2000. ISBN: 978-

0849318689.

N. Doraswamy, D. Harkins, “IPSec: The New Security Standard for the internet, intranets,

and Virtual Private Networks,” Prentice- Hall, 1999. ISBN: 978-0130118981.

W. Cheswick, Steven M. Bellovin, “Firewalls and Internet Security,” Addison-Wesley, 1994.

ISBN: 978-0201633573.

D. Marchette, “Computer Intrusion Detection and Network Monitoring,” Springer-Verlag,

2001. ISBN: 978-0387952819.

Vesna Hessler, “Communication Security,” Part 2 of Security Fundamentals for E-

Commerce, Artech House Publishers.

Healthcare Multimedia Networks


Course objectives:

The purpose of this course is to familiarize students with the concepts and principles of

designing multimedia networks in the field of health. This course examines the architecture of

the service delivery protocol and the latest developments in the field of multimedia Networks

with medical applications.

Headlines:

• A review of the courses of multimedia networks in medicine

• Basic multimedia concepts including medical pictures, ECG and more.

• Network design in the field of health

• Basic concepts of future generation networks and their applications in medicine

• Quality of service (services) of health information exchange

• Send multimedia on TCP / IP networks

• Submission of multimedia on networks

• Multimedia applications and protocols with a medical approach

• Multifunctional multimedia under IP and its applications in the field of health

• Send multimedia on wireless networks / sensor networks

• Medical multimedia applications under the network

• Security of multimedia networks

• Health content delivery networks

• New research topics

Grading


10% 25% Written Exam (50%) 15%

Practical Exam (0%)


ZeNian Li and Mark Drew, Fundamental of Multimedia, Prentice-Hall, 2003.

Roy Rada and Claude Ghaoui, Medical Multimedia, Intellect Books, 1995.

J. Kurose and K. Ross, Computer Networking: A Top-Down Approach 4th Ed. Addison-

Wesley, 2008.

H.J. Chao, X. Guo, Quality of Service Control in High-speed Networks, John Wiley and

Sons, 2002.

M. Van Der Schaar, P. Chou, Multimedia over IP and Wireless Networks: Compression,

Networking, and Systems, Academic Press, 2007.

Evolutionary Computing and Fuzzy system


Course objectives:

The purpose of this course is to familiarize students with the concept of evolutionary processing

(such as genetic algorithm) in solving search problems and fuzzy systems in uncertainty

modeling as well as deciding the tools of this domain. As students become acquainted with these

concepts, the ability to model the uncertainty and develop autonomous argumentative systems in

the field of health is expected to emerge.

Headlines:

•Evolutionary Computing

- Motivation to take advantage of the natural evolution phenomenon in problem solving

- Search spaces and fitness landscapes

- Comparison of genetic algorithms and common search methods

- Evolutionary programming

- Genetic programming

•fuzzy systems

- Fuzzy sets and its operators

- The principle of generalization

- Fuzzy numbers and their calculations

- Fuzzy relation

- Fuzzy graph

•Fuzzy Logic

- Fuzzy linear programming methods (with fuzzy goals, with fuzzy constraints, symmetric

models and fuzzy numbers)

- Decision making with fuzzy parameters

- Fuzzy group decision

- Fuzzy dynamic programming

•applications

Grading


10% 25% Written Exam (50%) 15%

Practical Exam (0%)


M. Mitchell, An introduction to genetic algorithms, MIT press, 1996.

D. Goldberg, Genetic algorithms in search, optimization and machine learning, Addison

Wesley, 1989.

D. Fogel, Evolutionary computation, IEEE press, 1995.

L. Davis, Handbook of genetic algorithms, Van Nostrand Reinhold, 1991.

J. Koza, genetic programming, MIT 1992.

H.J. Zimmermann, Fuzzy sets theory and its application, Kluwer academic pub, 1996

H.J. Zimmermann, Fuzzy sets, decision making and expert systems, Mc Graw Hill, 1987

Lai & Hwang, Fuzzy mathematical programming, Mc Graww Hill, 1992

Chen & Hwang, Fuzzy multiple attribute decision making, Prentice Hall, 1992.

K.P. Yung, C.L. Hwang, multiple attribute decision making, an introduction, Sage

publications, 1995.

J. Figueria, S. Greco, M. Ehrgott. Multiple criteria decision analysis, state of the art

surveys, springer, 2005.

Bioinformatics


Course objectives:

Advances in biotechnology have led to a huge leap in biotechnology and medicine. On the other

hand, this area has a rich source of data and information, and computational methods play a key

role in transforming these raw data into a deeper understanding of biological and biological

systems.

Bioinformatics (Computational Molecular Biology) involves the compilation and

implementation of computational methods for the management and analysis of information

derived from the sequence, structure and functions of the biological and system molecules. In

this course, the student provides algorithmic and statistical concepts for how to respond to

common needs and questions in the field. The biology data will be collected. Biological data can

be classified according to the level of available information: DNA, RNA, proteins, metabolites,

and other small molecules. This course is divided into different parts based on a specific type of

bio data, and each section presents the challenges of this type of data and computational methods

to respond to them.

The student is expected to become familiar with the following concepts at the end of this course:

• Bio-data types

• Computational issues related to the processing of biological data

• Familiarity with key algorithms in computational biology

Headlines:

• Introduction to molecular biology

• Genome assembling

- Genome compilation, greedy algorithm, overlapping consensus plan, graph theory

concepts, Debruijn graphs, Hamiltonian paths, Euler's path

- Volot algorithm for starting genomes

• Comparison and comparison of genomes

- Planning methods for topical and global matching

- Blast algorithm, intelligent methods for searching in sequence databases

Multiple sequencing, MSA issue

- Introduction to phylogenetic trees

- Parsimony methods for building a phylogenetic tree

- Probabilistic methods for constructing a phylogenetic tree

• Identification and segmentation of the genomes

Markov Models and CpG Islands Identification

- Hidden Markov models and their application in detecting and isolating genes

- Familiar with functional genomics, transcriptome, proteome, metabolome

- Hierarchical clustering methods

- Clustering methods based on probabilistic models

- Analyze the omics data set

• Analysis and conclusions for bio-networks

- Bio Networks

- Probabilistic graphs and Bayesian networks

- Methods for estimating parameters of Bayesian networks

- Lasso regression and regression models for biological networks

Grading


10% 25% Written Exam (50%) 15%

Practical Exam (0%)


R. Durbin, S. R. Eddy, A. Krogh, and G. Mitchison, Biological Sequence Analysis:

Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press, 1998.

N. C. Jones and P. A. Pevzner, An Introduction to Bioinformatics Algorithms, 1st ed. The

MIT Press, 2004.

J. Pevsner, Bioinformatics and Functional Genomics, 2nd ed. Wiley-Blackwell, 2009.

P. Baldi and S. Brunak, Bioinformatics: The Machine Learning Approach, Second Edition, A

Bradford Book, 2001.

D. Koller and N. Friedman, Probabilistic Graphical Models: Principles and Techniques, 1st

ed. The MIT Press, 2009.

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining,

Inference, and Prediction, Second Edition, 2nd ed. 2009. Corr. 3rd printing 5th Printing.

Springer, 2009.

S. M. Ross, Introduction to Probability Models, Tenth Edition, 10th ed. Academic Press,

2009.

P. Baldi and S. Brunak, Bioinformatics, 2nd Edition: The Machine Learning Approach. MIT

Press, 2001.

Probabilistic Graphical Models


Course objectives:

Probabilistic graph models provide a suitable template for modeling various bio-phenomena

using graph-based models and probabilistic representations of variables using graphs. In this

course, students will get acquainted with various frameworks including Bayesian networks,

random Markov squares. How to display probabilistic models, approximate and accurate

inferences for these models, estimating the parameters and structures of graphic models, Markov

networks, conditional random fields, and how to use these models in a large measure, are the

subjects discussed in this course.

Headlines:

• Introduction to probabilistic graphic models and the motivation to use them

• Introduction to prerequisites of probability theory

• Familiarity with algorithms of graph theory

• Bayesian networks:

- Symmetry and theorem

- The structure of the Bayesian networks and the subsequent reduction of the probability

table using them

- Independence mapping, full mapping and minimal mappings

• Dispersed Frequency Frequency Distributions, Sparse CPD

• Markov networks

- Gibbs Factories and Distributors

- Conditional independence

- The Einsung Dual Mode, Pairwise Ising Model

- Graph properties in Markov random fields

- Theorem for Markov networks, Homersley-Clifford theorem

- Equivalence of Markov Properties

- Operating graphs, log-linear models

- Focal Parameterization

• The relationship between Bayesian networks and Markov networks

• Conditional random fields and maximum entropy method

• Decomposable graphing models, trees

• Gaussian Graphic Models

• Learning and estimating parameters in Bayesian networks using full data

- Maximum likelihood method

- Learning structures

- Learn the parameters based on the scoring and Chu-Liu trees

• Learning on directional models using full data

• Approximate approximation in graphic models and EM algorithms

• Hidden Markov Models

- Introducing and Defining Hidden Markov Models

- Estimation of parameters using dynamic programming

- MAP method for estimating parameters

- Baum algorithm - Welch, Baum-Welch Algorithm

- Viterbi algorithm, Algorithm Viterbi

• Dynamic Bayesian and Kalman filter models

Grading


10% 25% Written Exam (50%) 15%

Practical Exam (0%)


D. Koller and N. Friedman, Probabilistic Graphical Models: Principles and Techniques, 1st

ed. The MIT Press, 2009.

Darwiche, Modeling and Reasoning with Bayesian Networks, 1st ed. Cambridge University

Press, 2009.

J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, 1st

ed. Morgan Kaufmann, 1988.

J. Whittaker, Graphical Models in Applied Multivariate Statistics, 1st ed. Wiley, 2009.

S. L. Lauritzen, Graphical Models. Oxford University Press, USA, 1996.

C. M. Bishop, Pattern Recognition and Machine Learning, 1st ed. 2006. Corr. 2nd printing.

Springer, 2007.

D. J. C. MacKay, Information Theory, Inference and Learning Algorithms, First ed.

Cambridge University Press, 2003.

Healthcare Information Systems


Course objectives:

The purpose of this course is to familiarize students with the principles and applications of

information systems and their importance in the health and medical industry. Students in this

field have a deep understanding of the types of information systems and their needs in the health

and medical fields such as clinical, laboratory, pharmacy, radiology, decision making and others,

with emphasis on their applications in health centers such as hospitals, limited surgical centers

and outpatient treatment. Clinics, health centers and doctors' offices. The student learns how

information systems can transform health services, and there are new ideas in new tools and

systems that can improve human health.

Headlines:

• Introduction to health informatics and the classification of information systems in this area

• Health Data Quality: Challenges, Problems and Solutions

• Health regulations and regulations

• History and evolution of health information systems

• Current and emerging clinical systems

• Obtaining and implementing new health information systems

• Standards for health information systems and inter-system information exchange

• Health Information Systems Support Technologies

• Security in health information systems

• Organization, management and monitoring of health information systems services (ITIL and

Cobit)

• Governance of Information Technology in Health Organizations

• Strategic planning and alignment of information technology

• Evaluation, value creation and leadership of health information systems in health organizations

Grading


10% 25% Written Exam (50%) 15%

Practical Exam (0%)


K. A. Wager, Frances W. Lee, John P. Glaser , Health Care Information Systems: A

Practical Approach for Health Care Management, 2nd

Ed., ISBN: 978-047038780

C. J. Austin, Stuart B. Boxerman, Information Systems For Healthcare Management,

Health Administration Press/AUPHA, 7th

Ed., (July 24, 2008) , ISBN: 978-1567932973

Documents

Curriculum of Medical Information Technology Engineering · - Types of wireless networks: Bluetooth, Infrared (IR), Wi-Fi, ZigBee, Mobile Networks, Satellite Networks - Sensor networks