GRADUATE SCHOOL OF INFORMATICSMEDICAL INFORMATICS GRADUATE PROGRAM


Courses

MEDICAL INFORMATICS COURSES

MIN 502 Introduction to Medical Informatics

This course presents an overview of medical informatics and its main applications. Primary topics include: Reasons for necessity of systematically processing data, information and knowledge in medicine and health care, benefits and current constraints of using information and communication technology in medicine and health care, medical informatics as a discipline, medical data and records, coding classification, database and reference models, interfaces, data acquisition, processing and exchange standards, medical knowledge, decision and diagnostic support, medical information systems, administrative, clinical and ancillary information systems, implementations and evaluations, telemedicine and internet applications, efficient and responsible use of information processing tools to support health care professionals practice and their decision making.

CORE

         

syllabus

MIN 528 Fundamentals Mathematics for Information Systems

The aim of the course is to acquaint the non-technical background graduate students with the fundamental theory and techniques of engineering mathematics. This will be achieved by teaching fundamental theory as well as application based homework using MATLAB tool. The course covers subjects like Basic Calculus (Functions, Continuity, Integrals, Derivatives, Sequences, Series, Differential equations and their numerical implementations); Linear Algebra; Optimization; Fourier Transformation; Fundamentals of Signal processing and Filtering. 


CORE

syllabus

MIN 545 Object Oriented Programming and Data Structures

Basic Object Oriented Principles will be discussed using a modern programming language i.e. Java. Theoretical approaches will be developed to implement Data Structures which is very important in algorithm development. The core of the class will depend on using Java. Although some reading is required, practice is more important in learning any programming language.  

CORE

MIN 590

Graduate Seminars

MUST

MIN 599

Master’s Thesis

MUST

MIN 699

PhD Thesis

MUST

MIN 589

Term Project

Must For Non Thesis 

MIN 503 Electronic Health Records and Coding

This course gives an overview of contemporary health records and then introduces computer based patient records/electric health records. Topics include data entry, minimum data sets, general applications of electronic health records (EHR), standards in health and medical informatics, importance of coding and standardization, clinical uses of CPR. Current applications in all areas of medicine; like use of CPR in primary care to specialized clinical/departmental information systems and HIS applications shall be given. Reasons for necessity of medical coding and classification will be described. Primary topics include history of classification, important classification systems like ICD, SNOMED, MESH, ICPC, CPT, and practical application and uses of these coding systems.

Technical Elective

MIN 505

Neuroimaging: Anatomy, Physiology and Function of the Human Brain

The course introduces all three aspects - anatomy, physiology and function- of neuroimaging, which is enlisted as a sub-field of neuroinformatics. Theoretical knowledge on neuroanatomy and function of the brain will be complemented by hands-on applications with the existing online data analysis packages. The anatomy of the brain will be studied over MR images using volumetric and shape based techniques. The physiology of the brain will be studied over cytoarchitecture. The function of the brain will be studied over pet-spect, meg, eeg, and fMRI, with more emphasis on fMRI.

Technical Elective

MIN 506 Advanced Neuroimaging with Magnetic Resonance Imaging

New techniques in structural and functional brain imaging will be studied. Structural neuroimaging methods such as voxel based morphometry, diffusion tensor imaging, probabilistic cytoarchitectonic maps are covered in detail. Functional neuroimaging methods such as independent component analysis, dynamic causal modeling, resting state networks and arterial spin labeling are studied. Hands on exercises will be conducted using publicly available neuroimaging toolkits.

Technical Elective

MIN 524 Fundamentals of Medical Imaging: Acquisition and Reconstruction

This course covers fundamental medical imaging modalities like X-ray, CT, MRI, SPECT, PET and Ultrasound. Physics and mathematical models of data acquisition and image reconstruction concepts are studied both theoretically and by implementation based homework assignments on MATLAB. Medical imaging system properties like detector noise, resolution, point spread function, modulation transfer function, sampling, contrast and lesion detectability are also discussed.

Technical Elective

MIN 530 Medical Image Analysis

The aim of the course is to acquaint the graduate students with the fundamental theory and techniques of medical image analysis, like image enhancement, automatic and semi-automatic image segmentation, image quantification (shape and texture analysis, feature extraction), computer-aided diagnosis, image alignment (registration) and fusion.

Technical Elective

syllabus

MIN 533 Brain Dynamics and Oscillations

This course introduces the tools of EEG and MEG data analysis to capture brain dynamics with fast temporal resolution. It will provide the relation between neuronal activity and electromagnetic mapping, necessary physics of measured sensor data, brain source localization and identification. Various preprocessing tools, time-frequency analysis, brain source reconstruction methods will also be covered. Theory will be complemented by hands-on sessions in which students will be tutored through the complete analysis of EEG and MEG datasets with popular free online data analysis packages. The functional image of the brain will be studied with simulated and real EEG and MEG data projected on MR images.

Technical Elective

MIN 535 Biological Signal Analysis

This course provides main tools to interpret the biomedical signals ranging from neural and cardiac rhythms to muscular activity. It takes a probabilistic signal processing approach and introduces traditional methods of time-frequency analysis as well as more recent issues of fractals, self-similarity, cross-frequency coupling and independent component analysis. Theory of methods shall be complemented with biomedical data applications emphasizing and motivating their use in practice.

Technical Elective

MIN 537 Neural Networks for Biomedical Applications

The course introduces “neural networks” mainly to train features from biological signals in order to classify patterns, construct models, predict outcomes and make decisions. Fundamental supervised and unsupervised neural network algorithms will be introduced and they will be applied on biological signals such as fMRI, EEG, MEG, EOG, EMG and ECG. Recent advances in the common space of artificial neural networks and biomedical signal processing will be covered during the course

Technical Elective

MIN 539 Bio-Inspired And Classical Optimization

Proper optimization is critical for various problems involving decision making. This course introduces ideas and methods to solve unconstrained and constrained optimization problems. The methods involve both traditional and biologically inspired approaches. While the former includes mainly gradient based methods and linear & convex programming, the latter covers diverse approaches from evolutionary computation to neural networks and swarm intelligence. Theoretical formulations of all methods will be presented indicating use, advantages and disadvantages of them. Complementarily, many computer exercises will be supplied to demonstrate their applicability for commonly observed problems.

Technical Elective

MIN 550  Systems Neuroscience

Electrical and chemical neural signaling is introduced. Subcomponents of the central nervous system are studied thoroughly with morphological and functional aspects. Sensory processing along the visual, auditory, olfactory, somatosensory pathways is discussed and laboratory experiments are performed for each tract. The motor circuits and autonomous nervous system are studied and clinical evidences are covered.

Technical Elective

MIN 555  Principles of Cognitive Neuroscience 

This course introduces the building blocks of cognitive neuroscience both methodologically and conceptually. Basic methods such as functional magnetic resonance imaging, electroencephalography, and eye tracking are studied in detail and the underlying physiological foundations in these measurements are discussed. Measurement of brain function using these tools are illustrated through hands-on exercises. Foundations of the brain such as language, memory, attention, executive function and their neuroscientific infrastructure are investigated conceptually. Clinical examples in psychiatry and neurology (eg. Schizophrenia, depression, Parkinson’s Disease) and active research areas (aging, development, resting state) are introduced.    

Technical Elective

MIN 701 Networking for Health Information Systems and Telehealth

The course summarizes the fundamentals of computer networking from a health informatics perspective and introduces the students to the underlying concepts of telehealth. Topics on computer networking include hardware and software components, protocol layers, application layer protocols, socket programming, Internet protocol, multimedia networking and local area networks. The subjects on telehealth are discussed starting by describing history, definitions and current applications. Then, the advantages and barriers for successful telehealth projects are emphasized, types of telehealth projects are discussed, and security and legal issues are given. More advanced topics such as virtual reality are also presented.

Technical Elective

MIN 702 Evaluation Methods in Health Informatics

Medical Informatics is a multifaceted interdisciplinary field. In this area clearly there is a need for clinical information system, good research design, carry out, measurement technique, analysis of studies, evaluation and interpretation of wide range quantitative and qualitative techniques. This course will be useful for all medical informatics professionals.

Technical Elective

MIN 703 Medical Imaging Applications

This course provides a basic overview of the applications of medical imaging and Radiology Information Systems (RIS). Practical applications of X-ray radiography, computed tomography, magnetic resonance imaging, ultrasound and ultrasonography, Doppler ultrasound and Doppler ultrasonography, computed radiology, digital radiology, radiology information systems and other medical imaging techniques are briefly introduced. Various image processing applications on medical images are introduced in both clinical and technical perspectives.

Technical Elective

MIN 704 Reasoning Under Uncertainty 

Uncertainty models and information representations: types of uncertainty (predictive, retrodictive, diagnostic, prescriptive) and uncertainty measures (incompleteness, imprecision, vagueness, inconsistency, dissonance, confusion, etc.). Entropy and set-theoretic representation of information (crisp sets, fuzzy measures like Belief functions and fuzzy sets). Minimization of uncertainty. Decision making under uncertainty. Applications to medical informatics.

Technical Elective

MIN 710 Database Applications For Medical Informatics

Database management system theory on relational database management systems: E-R diagrams, Normal Forms, Transaction processing. SQL data query language. Homework and class assignments on each of these topics, centered on examples from the health information systems. Object oriented databases, and their use in medical informatics. Archetypes, and use of semantic systems in HIS. Standards in medical informatics on interoperability and meta-data.

Technical Elective

MIN 711 Advanced Topics in Medical Image Analysis

This course is aimed for graduate students who want to do their thesis in the field of medical image analysis. Various advanced topics in medical image analysis are introduced: non-linear image enhancement, organ segmentation, lesion detection, Wavelet transform, feature extraction, computer-aided diagnosis, active shape model, multi-modal fusion, content-based image retrieval and augmented reality for surgical navigation.

Technical Elective

MIN 715 Future Studies for Healthcare

This course covers basic assumptions and theories; and reviews some of the most important trends and issues shaping the future and how we provide healthcare. Throughout the course four fundamental foresight skills: creating the future (innovating products and services); discovering the future (models, trend identification and analysis); planning the future (developing shared goals and processes); and benefiting in the future (achieving measurable positive environmental, social, or economic results) will be examined. The big picture of future studies will be explored through the history of accelerating changes from universal, historical and technological perspectives, and considering global trends that are affecting individuals, society, healthcare sector and other businesses and governments. Emerging approaches and future trends in healthcare and how biology, psychology, community and culture help and hinder personal thinking about the future will be discussed. How organizations can form calculated bets on the future will be examined, which will give student a chance to explore career prospects in a variety of fields.

Technical Elective

syllabus

MIN 717  Mobile Health

Integration of mobile technologies and healthcare informatics sets the stage for innovative emerging research discipline titled as ‘mhealth’ or ‘mobile health’. This course will include the basic ideas, tools, case studies and contributions of mobile in healthcare sector. The subjects to be covered will involve: mobile and wireless concepts; medical information for mobile health and management; patient monitoring in diverse environment and in hospital; medical body sensor networks; context aware systems; mobile health performance. The course will include class discussions of theoretical concepts and case studies, quizzes, critical reading assignments, a midterm exam and a term projects.

MIN 720 Pattern Classification for Bio-Medical Applications

Introduction to Pattern Classification as a tool for decision making. Statistical Approaches.  Bayes Classification. Parameter Estimation. Nearest Neighbor Rule. Linear Discriminant Functions. Neural Networks. Support Vector machines. Tree Classification. Multiclassifiers. Clustering. Various applications in biology and medicine.

Technical Elective

MIN 8XX

Special Studies

Special Studies

 

MIN 9XX

Advanced Studies

Advanced

Studies

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