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Course Schedule and Descriptions

Course #TitleInstructorCreditsInfo
Fall 2019 (schedule)
BMIF 6300Foundations of Biomedical InformaticsM. Frisse3Course Plan
BMIF 6310Foundations of BioinformaticsC. Lopez3Course Plan
BMIF 6321Scientific CommunicationT. Rosenbloom1Course Plan
BMIF 6331Student Journal Club and Research ColloquiumR. Carroll1Course Plan
BMIF 7340Clinical Information Systems and DatabasesD. Giuse (offered Fall 2020)3Course Plan
BMIF 7370Evaluation Methods in Biomedical InformaticsK. Unertl
J. Peterson
3Course Plan
BMIF 7391Special Topics: Technology and SocietyL. Novak (offered Fall 2020)3Course Plan
Spring 2020 (schedule)
BMIF 6315Methodological Foundations of Biomedical InformaticsD. Giuse
T. Lasko
3Course Plan
BMIF 6322Scientific CommunicationT. Rosenbloom1Course Plan
BMIF 6332Student Journal Club and Research ColloquiumR. Carroll1Course Plan
BMIF 6342Research Rotation in Biomedical InformaticsArrange with instructor/mentor1
BMIF 7311Systems BiologyTBD3Course Plan
BMIF 7320Healthcare System and InformaticsN. Lorenzi (offered Spring 2020)3Course Plan
BMIF 7380Data Privacy in BiomedicineB. Malin3Course Plan
BMIF 7391Special Topics: Data Management for Clinical & Translational ResearchP. Harris3Course Plan
CS3892Special Topics: Big DataD. Fabbri3
Research Courses
BMIF 7395Directed Research Independent Study
BMIF 7999Master Thesis Research
BMIF 8999Non Candidate Research
BMIF 9999PhD Dissertation Research

Class Descriptions

6300. Foundations of Biomedical Informatics

This introductory course examines the unique characteristics of clinical and life science data and the methods for representation and transformation of health data, information, and knowledge to improve health care. Principles of information security and confidentiality are taught, along with functional components of information systems in clinical settings and the use of databases for outcome management. Through skill modules, the course provides an introduction to methods underlying many biomedical informatics applications, including information retrieval, medical decision making, evaluation of evidence and knowledge representation. The historical evaluation of the field of biomedical informatics is taught concurrently, using examples of landmark systems developed by pioneers in the field. FALL [3] M. Frisse

6310. Foundations of Bioinformatics

This survey course introduces students to the experimental context and implementation of key algorithms in bioinformatics. The class begins with a review of basic biochemistry and molecular biology. Algorithms for matching, aligning, and comparing biological sequences will be evaluated in the context of molecular evolution. The class will examine the systems developed to enable high-throughput DNA sequencing and genome sequence analysis. The informatics associated with microarrays for the quantitative analysis of transcription will follow. Next, the course will consider the algorithms supporting proteomic mass spectrometry and protein structure inference and prediction. Finally, the class will examine the tools of systems biology, including genetic regulatory networks, gene ontologies, and data integration. Formal training in software development is helpful but not required. Students will write and present individual projects. Undergraduates need the permission of the instructor to enroll. FALL [3] C. Lopez

6311. Systems Biology

This survey course presents the student with the historical, conceptual and technical foundations of systems biology as it relates to biomedical research using model systems as well as human disease. Prerequisite: BMIF 6310 Foundations of Bioinformatics. SPRING [3] TBA

6315. Methodological Foundations of Biomedical Informatics

In this course, students will develop foundational concepts of computation and analytical thinking that are instrumental in solving challenging problems in biomedical informatics. The course will use lectures and projects directed by co-instructors and guest lecturers. SPR [3] D. Giuse, T. Lasko

6321. Scientific Communication

The course will enhance students’ skills in written and oral scientific communication. An introductory segment covers categories of scientific writing, the peer review process, and ethical issues in research communication. Through a two-semester sequence, it provides direct, hands-on experience in writing papers, abstracts, and grant proposals; critiquing and copy editing; and, preparing and giving presentations for scientific meetings. FALL [1] T. Rosenbloom

6322. Scientific Communication

See 6321. SPR [1] T. Rosenbloom

6341. Research Rotation in Biomedical Informatics

Students will perform research under the direction of a faculty advisor. FALL [1]

6342. Research Rotation in Biomedical Informatics

See 6341. SPR [1]

7320. Healthcare Systems and Informatics

The purpose of the Healthcare Systems and Informatics course is for students to understand the organizational world in which they will spend most of their professional lives. A better understanding will lead to strategies to build partnerships with physicians, researchers, hospitals and academic organizations. In turn, better understanding will lead to working more closely as a team in planning future directions and implementing technological programs and changes. This course provides an overview of theoretical concepts as well as the practical tools for the student to understand and work effectively with three main topic areas: (1) understanding health care organizations, especially academic health centers; (2) understanding the health care environment; and (3) understanding organizational informatics, including leadership and people issues. Prerequisite: BMIF 6300. SPR [3] N. Lorenzi

7330. Machine Learning for Biomedicine

This course builds on the material covered in Methodological Foundations of Biomedical Informatics (BMIF 6315) by introducing several additional machine learning concepts and algorithms with a focus on biomedical decision making and discovery. Even though biomedical applications and examples will be discussed, the methods have broad applicability in science and engineering. The following topics will be covered in this course (may be expanded or modified based on the background of the class participants): decision support systems, cognitive issues in decision making, Bayesian networks, data pre-processing for machine learning, decision trees, clustering, K-Nearest neighbors, neural networks, SVM regression and unsupervised SVMs, Bayesian network learning, and causal discovery using Bayesian networks. Prerequisites: for Biomedical Informatics students: BMIF 6315; for non-Biomedical Informatics students: a course in data structures or algorithm design and analysis, the ability to program in Matlab version 6 or later, and basic concepts of machine learning and fundamental mathematical concepts needed in machine learning at the level covered in BMIF 6315. SPR [3] Staff

7340. Clinical Information Systems and Databases

This course builds on material covered in Methodological Foundations of Biomedical Informatics (BMIF 6315) by introducing and developing concepts in distributed systems and network computing: OSI stack, protocols, TCP/IP, Sockets, and DNS; clinical database concepts: synchronization, concurrency, deadlock, full-text databases; distributed database services, including high-availability techniques; and architectural considerations in the design of clinical information systems. The VUMC clinical database architecture is used as a case study. Prerequisites: for Biomedical Informatics students: BMIF 6315 or permission of instructor; for non-Biomedical Informatics students: coding ability in some standard procedural or object-oriented computer language, preferably PERL. FALL [3] D. Giuse

7360. Graduate Seminar on Biomedical Informatics Algorithms

Graduate-level topics in intermediate or advanced algorithms, data structures, and knowledge representations for biomedical informatics that are not covered in the MS/PhD core courses. Note: covered topics will be highly dependent on faculty and student interests and will change from year to year to reflect research advances and interests. Students must obtain instructor permission to enter the class. [1-3] (Not currently offered.)

7999. Master’s Thesis Research

7370. Evaluation Methods in Biomedical Informatics

Students are introduced to evaluation and experimentation, with exposure to study design, including sampling, appropriate use of controls; data collection, including human subjects research considerations; analysis, including testing for statistical significance, definitions of sensitivity and specificity, ROC plots; and reporting of results. Quantitative and qualitative methods will be covered, as well as methods and issues specific to healthcare settings. FALL [3] K. Unert, J. Peterson

8999. Non-Candidate Research

7380. Data Privacy in Biomedicine

This course introduces students to concepts for evaluating and constructing technologies that protect personal privacy in data collected for primary care and biomedical research. Material in this course touches on topics in biomedical knowledge modeling, data mining, policy design, and law. Prerequisite: Students are expected to be proficient in writing basic software programs; although no specific programming language is required. SPR [3] B. Malin

7391. Special Topics: Technology and Society

This course engages students in discovering relationships among individuals, institutions, and technologies, and how those relationships evolved in specific cultural contexts. Students and instructors will explore this topic in four modules: 1) understanding health care actors and technologies; 2) institutions and other infrastructures, including scientific disciplines, government, and information infrastructures; 3) principles of ethics and their application in biomedical informatics research and practice; and 4) integration of the concepts. The course will be conducted as a seminar, in which students and instructors will discuss assigned readings and films. Each student will present a final case to the group.  FALL [3] L. Novak

7395. Directed Research / Independent Study

Students will work under close supervision of a specific faculty member on an ongoing research problem. Depending on the specific project, students will learn aspects of study design, research methods, data collection and analysis, research manuscript writing, and human factors engineering. SPRING/FALL [1-3] Staff.

9999. Ph.D. Dissertation Research

CS3892. Special Topics: Big Data

This course provides an introduction to the principles and practices of “big data”. Topics include: data storage, SQL vs. NoSQL databases, data models, data processing and querying, map reduce, data analytics and applications of machine learning.  This course is for both senior undergraduates and graduate students. The course requires students to read research papers and write code.  SPR [3] D. Fabbri