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Graduate Program in Biomedical Informatics

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Brochure (pdf file)     Executive Summary (pdf file)

The Vanderbilt Biomedical Informatics Graduate Degree Program

Program Goals

The main goal the Vanderbilt Biomedical Informatics Graduate Degree Program aspires to achieve is Academic Excellence: To provide a Biomedical Informatics degree program adhering to the highest academic standards. This program will meet the increasing local and national demand for high-quality professionals educated in biomedical informatics, and contribute to the improvement of healthcare research and delivery for the benefit of patients and society.

To achieve this main goal the program will need to attain the following sub-goals:

Goal 1 - Student Excellence: To attract highly-qualified graduate students who will develop innovative research projects and catalyze research and service efforts in the existing biomedical informatics and bioinformatics projects and units on campus.

Goal 2 - Research Excellence: To facilitate innovative, state-of-the art biomedical informatics research that has significant theoretical and applied impact, and to increase research collaborations.

Goal 3 - Educational Excellence: To design and implement innovative curricula that will address important educational challenges. In particular, curricula that (a) will make it worthwhile for students to postpone highly-compensated employment to pursue advanced academic education in today's highly competitive job market; (b) will provide education that through flexible adaptation to the students' needs and goals, can lead to success in as diverse post-graduation pursuits as academics, R&D or scientific management in large corporations, or entrepreneurial endeavors, and (c) will instill students with not only the ability and confidence to achieve high-goals but also the ethical responsibility, social consciousness and compassion that has to characterize professionals working in the healthcare field.

Focus of the Vanderbilt Biomedical Informatics Graduate Degree Program

Since our explicit goal is to create a program of outstanding quality, we focus on selected areas for which there is demonstrable expertise and achievements among current faculty (consisting of DBMI/Core-BMIP faculty plus affiliated faculty from collaborating units across the VU campus). This focus is implemented by offering six Ph.D. concentration areas, while M.S. curricula will be uniform across students with same backgrounds:

Area I: Decision-Support Systems & Medical Decision Sciences Concentration Area
(a) Decision-Support Systems, and Medical Artificial Intelligence.
(b) Computer-assisted discovery, Machine Learning, Data Mining.

Area II: Evidence-Based Practice Informatics Concentration Area
(c) Formal Medical Decision Making Theory, Medical Cognitive Biases.
(d) Digital libraries, analysis of biomedical information needs, literature databases and retrieval, biomedical information evaluation and classification.

Area III: Health Policy Informatics, Management, and Administration Concentration Area
(e) Organizational aspects of informatics, quality assessment, systems management and evaluation, health care policy informatics, quality assurance and continuous quality improvement; ethical and legal issues in informatics.

Area IV: Clinical Systems Concentration Area
(f) Hospital Information Systems, Electronic Patient Records, Order-entry and reminder/alerting systems, and related technologies for high-performance, distributed, multimedia databases.
(g) Nomenclature, coding methods, standards; natural language understanding and processing of clinical records.
(h) Computer record confidentiality and security.

Area V: Bioinformatics For Molecular Medicine Concentration Area
(i) Development of algorithms and software systems to manage and analyze complex genomics and proteinomics data.

Area VI: Clinical Bioinformatics Concentration Area
(j) Translation of basic bioinformatics research to clinical practice.

Concentration areas I and II are only distinguished by research focus, qualifying exam, and electives. All other concentration areas have different core requirements even for students with same background (see example curricula templates). We note that concentrations V and VI do not constitute complete programs in Bioinformatics, but rather, concentration areas specially designed to facilitate research in bioinformatics and integrate results from bioinformatics as the latter applies to biomedicine through clinical information systems.

Curriculum Principles

The proposed curriculum is founded on four high-level principles:

  • Balance between theory and research, and between breadth and depth of knowledge.
  • Teaching and research excellence (by placing emphasis on student and teaching quality rather than quantity, by concentrating on targeted areas of biomedical informatics, and by close student guidance and supervision).
  • Developing leadership, and
  • Student-oriented curriculum design (created around student needs, background, and goals, and aiming at long-term competence using a combination of broadly-applicable methodological knowledge, and a strong emphasis on self-learning skills).

Curriculum Design, Structure, and Degree Requirements

Curriculum Design

The Vanderbilt Biomedical Informatics Graduate Degree Program integrates knowledge and skills from:

  1. Informatics and computer science (e.g. computer organization, computability, complexity, operating systems, networks, compilers and formal languages, data bases, software engineering, programming languages, design and analysis of algorithms, data structures).
  2. Biologic sciences and medical sciences (includes principles of cell, organism, and population biology, anatomy, physiology, and mechanisms of disease; nosology, diagnostics, therapeutics, genetics, and molecular medicine).
  3. Mathematical sciences (includes research design, epidemiology, and systems evaluation; mathematics for computer science (discrete mathematics, probability theory), mathematical statistics, applied statistics, mathematics for statistics (linear algebra, sampling theory, statistical inference theory, probability)).
  4. Biomedical informatics (includes methods from the social sciences (e.g.organizational management, evaluation, ethics, health policy, communication, cognitive learning sciences, psychology, and sociological knowledge and methods.), evidence-based practice, decision-support systems in biomedicine, clinical information systems, and informatics for bioinformatics).

To achieve in-depth learning of the above knowledge and skills we adopt a student-oriented curriculum design, whereby we identify "teaching or learning processes", that is, structured activities geared towards learning (i.e., courses/projects/assignments, seminars, examinations, defenses, theses, teaching requirements, directed study, research, service, internships). These processes were selected, adapted, or created in order to meet a set of pre-specified learning objectives that were identified by the faculty as being important for graduates to master: The Courses section discusses these objectives and maps them to courses.

Figure 4 presents pictorially the 3-level design of the curriculum:

Curriculum Structure

The curriculum is structured as follows:

  1. All students will take five core Biomedical Informatics courses (Foundations of Biomedical Informatics and Evidence-Based Medicine, Medical Decision-Support Systems and Machine Learning, Health Care Organization and Management, Bioinformatics for Molecular Biology, Clinical Information Systems). Those who are pursuing an M.S. degree will not be required to take the four associated laboratories, while those who pursue a Ph.D. must do so.
  2. All students will take core courses in the remaining three areas (3 courses for the methods area, 3 for the biomedicine area, and 3 for the informatics area). Course requirements in each area may be waived according to student background, upon review by the faculty.
  3. Students will be required to take additional electives within one or more areas so that they will have at least 27 credits of formal coursework for the M.S. level. A minimum total of 27 credits will be required for a thesis M.S. degree.
  4. For the M.S. degree, a research project of appropriate scope should be planned, proposed, carried out, and defended according to University rules. M.S. degree students must complete a written thesis. A published paper may serve as the thesis at the discretion of the advising committee. In case of Ph.D. students pursuing a Bioinformatics for Molecular Medicine or Clinical Bioinformatics concentration area, a M.S. degree may be awarded with no thesis provided that the thesis requirement will be replaced by a minimum of 9 additional formal course credits (subject to approval by the program faculty).
  5. All students must participate in the Informatics Center research seminars.
  6. M.S. Students may take internships at external organizations during the summer of year 1. In addition to the above, students pursuing a Ph.D. degree will be required to serve as teaching assistants for at least one course, or, if their professional interests lie in positions in industry, to take an additional elective on management, economics, or entrepreneurial skills (subject to committee approval). Also, Ph.D. students must:
  7. Create a reading list on three areas of concentration. The list must be approved by an advising committee. After an agreed amount of time, the student will be given a written, take-home, week-long exam. After successful completion of the written examination, the student will have to pass an oral exam (typically covering material extending the scope of the written exam).
  8. Select an advising committee for the doctoral research project, prepare a research plan, execute the research, prepare a written thesis, defend the thesis, and publish the results, as per University rules.
  9. Take two additional advanced elective courses. Finally, those students pursuing a part-time M.S. degree (i.e., working at a 50% or greater level of effort external to the VU-BMIP), will be required to meet coursework requirements during the first two years of their studies and the research requirements within 1 to 2 subsequent years. The option of a combined M.D./M.S.-BMI program is also available. As a general comment the amount of formal coursework required for the MS and PhD degrees will vary depending on student prior background and concentration area (27 to 40 credits for the MS and 33 to 46 for the PhD). The absolute number of total and formal course credits for the MS and PhD degrees is comparable to other programs in the basic and engineering sciences (e.g., Neuroscience, Computer Science).


Course categories and offerings

Courses are in general divided into four main categories: Informatics & Computer Science Includes computer science topics (computer organization, computability, complexity, operating systems, networks, compilers and formal languages, data bases, software engineering, programming languages, design and analysis of algorithms, data structures).

The Essential Informatics Courses (including program entrance prerequisites) are:

  1. Computer Programming
  2. Design and Analysis of Algorithms and Data Structures (e.g., CS 250, or CS201)
  3. Networks (e.g., CS 283)

Students may also take electives on such topics as systems management, data mining, or operations research, as needed by their research and career focus. We note that in this and all four course categories "essential" courses refer to the material/skills that is important for students to know/master. In some cases this material will have been covered before entering the program, in other cases in the program. This explains why in some cases undergraduate-level courses appear as essential program courses. We will not give graduate credit however for more than one undergraduate-level essential course taken by any degree candidate. For a list of informatics-related courses currently offered within the university that are pertinent to the proposed program please see here.

Biology and Clinical Studies

Includes principles of cell, organism, and population biology, anatomy, physiology, and mechanisms of disease; nosology, diagnostics, therapeutics, genetics, and molecular medicine.

The Essential Biology and Biomedical Courses (including program entrance prerequisites) are:

  1. Overview of Biology (e.g., Biology 201)
  2. Human Gross Anatomy and Physiology (e.g., Biology 258, Cell Biology 321, NS 210ab, or BME equiv)
  3. Introduction to Medical Diagnosis and Therapeutics (NS 304ab)

Students may also take electives on such topics as genetics, laboratory techniques, or bioengineering, as needed by their research and career focus.

Mathematical Methods

Includes research design, epidemiology, and systems evaluation; mathematics for computer science (discrete mathematics, probability theory), mathematical statistics, applied statistics, mathematics for statistics (linear algebra, sampling theory, statistical inference theory, probability).

The Essential Mathematical Methods Courses (including program entrance prerequisites) are:

  1. Research Design (may be substituted by Epidemiology, Clinical Trials, or Systems Evaluation depending on student focus) (e.g., PHD 311P)
  2. Mathematical Statistics (e.g., Mathematics 218, 218L)
  3. Multivariate Statistics (e.g., , NS 397) Students may also take electives on such topics as advanced calculus, analysis, or stochastic processes, as needed by their research and career focus.

Biomedical Informatics

Includes core and elective courses that provide the necessary interdisciplinary focus and integration to the other components as well as teach methods that are particularly useful and prevalent in this field.

There are 5 Core Biomedical Informatics Courses (CBICs) that every student of the proposed program must take:

CBIC #1 : Bioinformatics for Molecular Biology (Course Coordinator: Mary E. Edgerton, M.D., Ph.D.)

CBIC #2 : Medical Artificial Intelligence I: Decision-Support Systems and Machine Learning for Biomedicine (Course Co-Coordinators: C.F. Aliferis M.D., Ph.D., R.A. Miller M.D., D. Aronsky, M.D., Ph.D.).

CBIC #3 : Foundations of Biomedical Informatics and Evidence-Based Practice (Course Coordinator: J. Ozbolt, Ph.D., R.N.).

CBIC #4: Clinical Information Systems and Databases (Course Coordinator: D. Giuse Dr.Ing.).

CBIC #5: Healthcare Organization and Management (Course Coordinator: N. Lorenzi, Ph.D.).

Advanced Elective Biomedical Informatics Course 1: Medical Artificial Intelligence II (Course Coordinator: C. Aliferis M.D., Ph.D.).

Advanced Elective Biomedical Informatics Course 2: Advanced Biomedical Informatics (Course Coordinator: R.A. Miller M.D.).

The faculty will offer additional electives from time to time to address new and interesting topics.

In addition, students will attend the following seminars:

Research Seminar & Colloquium.

Professional Skills Seminars
  • oral scientific communication
  • written scientific communication
  • ethics in biomedical research & academia
  • grant writing, and academic promotion
  • negotiation
  • consulting skills
  • Program-Related Courses Currently Offered Within Vanderbilt (Taken from the 1999-2000 Graduate School Bulletin)


    • Introduction to Linguistics
    • Medicine, Culture, and the Body


    • Advanced Genetics: Biochemistry and Cell Biology
    • Scientific Communication


    • Introduction to Cell Biology
    • Human Physiology
    • Statistical Methods in Biology

    Biomedical Engineering

    • Systems Physiology
    • Signal Measurement and Analysis
    • Introduction to Biomedical Computing
    • Bioelectric Signal Processing
    • Dynamics of Physiological Systems
    • Medical Imaging Neural Networks
    • Special Topics

    Biomedical Sciences

    • Responsible Conduct in Research I and II

    Cell Biology

    • Cell Biology
    • Gross Anatomy

    Computer Science

    • Computer Organization
    • Algorithms
    • Theory of Automata, Formal Languages, and Computation
    • Linear Optimization
    • Artificial Intelligence
    • Introduction to Database Management Systems
    • Project in Artificial Intelligence
    • Programming Languages
    • System Simulation
    • Compiler Construction
    • Software Engineering
    • Principles of Operating Systems I
    • Computer Networks
    • Computer-Systems Analysis
    • Design and Analysis of Algorithms
    • Algorithms for Parallel Computing
    • Large-Scale-Database Management Systems
    • Topics in Theory of Database Systems
    • Computer Vision
    • Advanced Artificial Intelligence
    • Machine Learning
    • Intelligent Tutoring Systems
    • Topics in Knowledge Engineering
    • Topics in Artificial Intelligence
    • Performance Evaluation of Computer Systems
    • Topics in Software Engineering


    • Intermediate Microeconomic Theory
    • Intermediate Macroeconomic Theory
    • History of Economic Thought
    • Economics of Health
    • Health Economics
    • Time Series Economics

    Education and Human Development

    Leadership and Organizations

    • Leadership Theory and Behavior
    • Inquiry
    • Images and Issues in Organizations
    • Qualitative Research Methodology
    • Personnel Administration
    • Computer-Based Educational Systems
    • Information Management Systems
    • The Nature and Function of American Higher Education
    • Literature and Research in Higher Education


    Teaching and Learning

    • Sociology of the Classroom
    • Psychological Foundations of Education

    Electrical Engineering and Computer Science

    • Image Processing
    • Signal Measurement and Analysis
    • Introduction to Robotics
    • Informatics Engineering
    • Neural Networks
    • Intelligent Tutoring Systems
    • Computer Vision


    • Organization Management and Human Resources
    • Work Team Management Operations Research
    • Introduction to Telecommunications Management
    • Advanced Telecommunications Management
    • Wireless Network and Mobility
    • Stochastic Processes

    Management of Technology

    • Informatics Engineering
    • Technology Assessment and Forecasting
    • Theory and Practice of Managing Technology
    • Technical Project Management
    • Quality Management


    • Linear Algebra
    • Discrete Structures
    • Discrete Mathematics
    • Introduction to Mathematical Statistics
    • Statistics Laboratory
    • Introduction to Applied Statistics
    • Advanced Engineering Mathematics
    • Probability
    • Mathematical Statistics
    • Introduction to Mathematical Logic
    • Advanced Calculus
    • Nonlinear Optimization
    • Linear Optimization
    • Introductory Analysis
    • Partial Differential Equations

    Molecular Physiology and Biophysics

    • Human Genetics


    • Neuroscience

    Nursing Science

    • Scientific Basis of Nursing Therapeutics
    • Comparative Research Methods
    • Qualitative Research Methods
    • Research Practicum.
    • Multivariate Statistics for the Health Sciences


    • Biostatistics Short Course


    • Formal Logic and Its Applications
    • Philosophy of Education
    • Ethics and Medicine
    • Philosophy and Medicine: I
    • Philosophy and Medicine: II


    • Neuroscience
    • Principles of Experimental Design
    • Quantitative Methods
    • Learning and Memory
    • Thinking and Cognition
    • Psychology of Language
    • Neural Network Models of Cognition
    • Cognitive Science
    • Models of Human Memory
    • Quantitative Methods and Experimental Design
    • Factor Analysis and Structural Equation Modeling
    • Clinical Research

    Psychology and Human Development

    • Statistical Inference
    • Experimental Design
    • Multivariate Statistics
    • Human Cognition


    • Ethics, Law, and Medicine
    • Genetics and Ethics


    • Social Problems of American Medicine
    • Human Behavior in Organizations
    • Schools and Society: The Sociology of Education
    • Multivariate Analysis I
    • Multivariate Analysis II
    • Quantitative Methods Workshop



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