Ph.D. Curriculum, Biomedical Informatics

The Ph.D. in Medical Sciences Degree with a Concentration in Biomedical Informatics is a 90-credit hour program, with at least 39 credits specific to the concentration.

Please visit our Course Schedule page to see when courses are scheduled to be taught.

The curriculum is comprised of:

  • 7 Core Courses (20 credits)
  • 6 Foundation Courses (6 credits)
  • 5 Statistics Course (3 credits)
  • Advanced Electives (11 credits)
  • Research (49-50 credits)

Core Courses – 20 credits

Course Name Description Timing/ Credits
GMS 6850: Foundations of Biomedical Informatics Biomedical informatics is the science of information as studied in or applied to biomedicine. Its scope therefore is information in patient care, public health, biomedical research, and health sciences education. This course will cover foundational issues such as the nature of information, how biomedical information is unique, the purposes for which biomedical information is created and used, the methods of analysis and inference on biomedical information, and the ethical and privacy issues involved. A   survey of various kinds of information systems and software in biomedicine and issues in their implementation and use will follow, including but not limited to electronic health records, software for analysis of genomic/genetic data, and public health surveillance systems. Fall Year 1 / 3 credits
GMS 6804: Translational Bioinformatics This course covers the fundamental issues of bioinformatics and how they apply to translational and clinical problems. The course is organized in 4 parts: sequence analysis, databases and ontologies, genome- wide association and linkage analysis, and networks. Each part will include coverage of the computing and mathematical concepts used, and motivated by the actual underlying bioinformatics questions. Spring Year 1 / 3 credits
GMS 6803: Data Science in Clinical Research Students are introduced to the broad landscape of data science for biomedical and clinical research: learn how to design and implement computerized databases for data collection, perform basic query and reporting operations, prepare databases for analytical tasks, perform quality assurance procedures, and understand basic data analytical methods and approaches. Fall Year 1 / 3 credits
GMS 6805: Introduction to Applied Ontology Applied ontology is a sub-discipline of knowledge representation that develops resources to make the meaning of terms accessible to computers, to improve interoperability of data, and to support reasoning with digital knowledge bases. This course introduces students to the fundamentals of applied ontology and its role in biomedical informatics. Students will learn what ontologies are, how they differ from similar resources, and how to build, evaluate, and query ontologies. Students will also gain familiarity with a variety of ontologies and their application in biomedical informatics. Fall Year 2 / 3 credits
GMS 6806: Security and Privacy in Clinical Research This course provides students with an introduction to a wide range of concepts, policies, and techniques in security and privacy (S&P) as they apply to biomedical and clinical research. Students are introduced to the broad landscape of information security and data privacy for biomedical and clinical research: learn S&P related regulations and guidelines in clinical research, understand basic concepts of computer security, perform security analysis of a study’s security plan, learn best practice in secure data management (e.g., de-identification and encryption), and gain insight into state-of-the-art tools, methods, and approaches for the protection of information security and data privacy. Spring Year 1 / 3 credits
GMS 7003: Responsible Conduct of Biomedical Research Key issues in the responsible conduct of biomedical research, following the research process from inception to planning, conducting, reporting, and reviewing biomedical research. (Part of Overall Medical Sciences Ph.D. Curriculum) Spring Year 1 / 1 credit
GMS 7887: Health Outcomes and Biomedical Informatics Research Seminar Presentations of research programs of graduate students and faculty in Health Outcomes and Policy and Biomedical Informatics with critical appraisal and discussion. Years 3 & 4 / 1 credit each in 4 semesters

Foundation Courses – Select 2 Courses (6 credits)

Course Name Description Timing/Credits
GMS XXXX: Clinical Decision-Making This course covers the fundamentals of decision theory: decision under uncertainty, decision under risk, and multi-criteria decision-making, including elementary aspects of game theory, and how they apply to evidence-based clinical decision- making. The emphasis of the course is on presenting theoretically sound algorithmic solutions to making evidence-based decisions in a clinical/biomedical context. Fall / 3 credits
BME 6938: Introduction to Biomedical Image Analysis and Imaging Informatics The end goal for this class is for you to not only learn the scope and importance of the fundamental principles of utilizing computers to analyze and manage images automatically, but also get hands-on training in developing accurate biomedical image analysis algorithms and suitable imaging informatics tools to solve the computational problems in your research. This class would be suitable for a broad spectrum of engineering students who are interested in learning how to utilize informatics and computational methods to analyze biomedical images and videos (multiple dimensions, multiple modalities) in a quantitative, automatic, objective, and high throughput manner. 3 credits
CEN 5035: Software Engineering Software Engineering, is a graduate- level introductory survey course on the fundamental concepts and principles that underlie current and emerging methods, tools, and techniques for the cost-effective engineering of high-quality software systems. The course focuses on surveying some of the critical aspects of SE that may be less familiar to students of computer science, such as identifying a development process appropriate to the circumstances, eliciting and documenting requirements, identifying appropriate design techniques, employing effective verification and validation strategies (e.g., reviews and inspections, formal methods) throughout the software development lifecycle, software maintenance, and software project management. 3 credits
COP 5725: Database Management Systems This lecture provides students with the essential concepts, principles, and techniques of modern database systems from a user perspective. This means that the lecture focuses on the functionalities that are offered by database systems and not on the methods to implement them. Specifically, the course teaches students the ability to develop a solution to a real-world data management problem that requires the application of the theories and practices developed in class. From a theoretical point of view, this course covers the essential principles for the design, analysis, and use of computerized database systems. The design and techniques of conceptual modeling, database modeling, database system architecture, and user/program interfaces are presented in a unified way. 3 credits
GMS 6822: Measuring and Analyzing Health Outcomes This course provides instruction in the measurement methods currently used in health-related research in both clinical and community settings. The particular emphasis in this course is on cross-cultural translation of patient- reported outcomes measures and the development and use of measures for special population such as children or those with chronic diseases. 3 credits
PHC 6405: Theoretical Foundations of Public Health / PHC 6001: Principles of Epidemiology in Public Health / Other relevant graduate level course if approved by the program director Variable 3 credits

Statistics Courses – Select 1 Course (3 credits)

Course Name Description Timing/Credits
GMS 6861: Applied Biostatistics Basic probability and distribution concepts and statistical analysis methods, including descriptive measures, point estimation, hypothesis testing (e.g., t test, analysis of variance, chi-square test etc.), confidence interval, simple linear regression and some nonparametric methods. SPSS will be used for basic statistical analyses. 3 credits
PHC 6050C: Biostatistical Methods Biostatistical data analysis using linear models; theory and practice of regression and analysis of variance in the health sciences. 3 credits
STA 6166: Statistical Methods in Research Introduce basic data analysis tools and to train graduate students in statistical tools associated with hypothesis testing and linear models. The aim is to promote sound scientific research and experimentation based on good statistical thinking and practice. 3 credits
STA 6826: Stochastic Processes I Discrete time and state Markov process. Ergodic theory. 3 credits

Advanced Electives – 11 credits

Course Name  Description  Timing/Credits
GMS XXXX: Semantic Web Technologies in Healthcare and Biomedical Research This course introduces students to a wide range of Semantic Web Technologies that are used for representing and linking biomedical information. The complex nature of biomedical research and research data urges a new paradigm like Semantic Web for knowledge representation. A thorough understanding the Semantic Web stack (e.g. standards, tools and resources) will prepare students to take advantage of the “big-data era” in the biomedical domains. This course will provide a basic introduction into the use of Semantic Web technologies. 3 credits
CAP 5100: Human-Computer Interaction A study of the major topics in human- computer interaction, including interface design (principles, theories), software tools, virtual environments, interactive devices, collaboration, and visualization. 3 credits
CAP 5635: Artificial Intelligence Concepts This course presents an overview of Artificial Intelligence concentrating on the core concepts of problem solving, heuristic search, and knowledge representation. It introduces one of the two major A.I. languages, Lisp, (the other being Prolog) and examines the application of the core concepts in some of A.I.’s application areas: natural language processing, expert systems, vision, planning, and learning. 3 credits
CAP 6610: Machine Learning The course will cover concepts involved in developing computer programs that learn, with an emphasis on methods based on probability and statistics and optimization, in particular linear models for regression and classification and kernel methods. Some fundamental questions involving the capabilities of these systems will then be addressed in the context of Statistical Learning Theory. Mixture models, including those involved in combining models, decision trees, and the Expectation- Maximization algorithm will also be covered. Methods for performance evaluation and training, including re- substitution, cross-validation, bagging, boosting, and bolstered error estimation will be discussed. 3 credits
COP 5725: Database Management Systems This lecture provides students with the essential concepts, principles, and techniques of modern database systems from a user perspective. This means that the lecture focuses on the functionalities that are offered by database systems and not on the methods to implement them. Specifically, the course teaches students the ability to develop a solution to a real-world data management problem that requires the application of the theories and practices developed in class. From a theoretical point of view, this course covers the essential principles for the design, analysis, and use of computerized database systems. The design and techniques of conceptual modeling, database modeling, database system architecture, and user/program interfaces are presented in a unified way. 3 credits
COT 5405: Analysis of Algorithms In this course, we will study various algorithmic paradigms (such as divide-and-conquer, greedy, dynamic programming), various analysis techniques (such as worst-case, expected, approximate), various problem domains (such as searching, sorting, graph theory, geometric, and combinatorially hard) problems. 3 credits
COT 5615: Mathematics for Intelligent Systems Topics covered:
Real analysis: basic point set topology, convergence, and continuity.
Vector spaces and linear algebra.
Mathematical probability theory: sigma algebra, random variables etc.
Information theory: entropy, mutual information, etc.
3 credits
COP 5618: Concurrent Programming Overview of principles and programming techniques. Reasoning about concurrency, synchronization, program-structuring, multi-threaded server applications. 3 credits
BME 6938: Machine Learning for Health and Biomedical Applications This is a graduate level course designed for students with no prior machine learning experience. It explores major concepts of machine learning and their application in healthcare and biomedical applications. 3 credits
GMS 6862: Applied Biostatistics 2 OR STA 6167: Statistical Methods in Research Variable 3 credits
GMS 7093: Introduction to Clinical and Translational Research Design, management, measurement, and study limitations for research in the clinical setting. 2 credits
PHI 5135: Graduate Logic Propositional calculus, quantificational logic through completeness, and an introduction to modal logic. 3 credits
STA 5325: Fundamentals of Probability This course introduces probability to students who have passed 3 semesters of undergraduate level calculus. Major topics include the basic formal elements of probability, discrete and continuous random variables, multivariate distributions, distributions of functions of random variables, and fundamental limit theorems. 3 credits

Research – 49-50 credits

  • GMS 7979: Advanced Research
  • GMS 7980: Research for Doctoral Dissertation

*Note: Students will need to have their degree plan approved by the program coordinator.