BMI Courses
100 Level | 200 Level | 300 Level | 400 Level | 500 Level
100 Level Courses
101 Introduction to Bioinformatics. (3)
Introduction to existing and future applications in bioinformatics. Introduces topics in translational bioinformatics, such as sequence alignment, the Human Genome Project, genomics, proteomics, gene expression analysis, genome wide association studies and next generation sequencing.
102 Introduction to Public Health/Imaging Informatics. (3)
Introduction to existing and future applications in public health and imaging informatics. This is the second course in a three semester series which focuses on biomedical informatics applications in public health (such as population studies and outbreak detection) and medical imaging, including computer-assisted radiological diagnosis, biomedical image processing and telemedicine.
200 Level Courses
201 Introduction to Clinical Informatics. (3)
Introduction to existing and future applications in clinical informatics. This third course in the series provides an introduction to biomedical informatics techniques and applications that aim to improve the quality and efficiency of clinical care. These includes searching and organizing free text information, decision analysis techniques and clinical decision support systems, and clinical applications including physician order entry used in electronic medical records. This course also covers challenges in clinical informatics, including socio-technical and cognitive issues in implementation and use.
211 Modeling Biomedical Decisions. (3)
The first semester of a three semester course sequence surveying the methods and theories underlying the field of biomedical informatics. This segment of the course explores models of medical decision making including classical decision theory, Bayesian and cognitive models. This aspect of the course focuses on the question of how best to model and support decisions faced by practitioners in the course of clinical care. The course will also include a component discussing evaluation methods in biomedical informatics, which will explain the various methods used to evaluate the accuracy and usefulness of biomedical informatics systems.
221 Knowledge Representation for Biomedical Informatics. (3)
Introduction to topics in knowledge representation and modeling, including frame-based systems, logic-based systems, rule-based systems, inference, and reasoning. As medicine is a knowledge-rich domain, systems designed to support performance of practitioners require some representation of this domain knowledge. This course presents the key formalisms that have been used encode knowledge into historical and contemporary biomedical informatics systems.
300 Level Courses
301 Clinical Environments. (3)
This three-credit course is designed for medical informatics students who have no significant clinical experience in the U.S. The course will introduce medical terminology and expose students to the clinical environments in which health care providers create, manage, and use clinical information. Students will be expected to attend lectures and will spend a significant portion of their time examining and reporting on different clinical settings throughout the semester.
311 Modeling Biomedical Knowledge. (3)
This course introduces concepts of artificial intelligence and knowledge modeling using medical informatics examples. Students will understand the historical foundations and motivations of AI in medical applications, learn how problem-solving, reasoning, knowledge management, and planning can be applied to medical informatics problems.
312 Modeling Biomedical Data. (3)
The third semester of a three semester course surveying the methods and theories underlying the field of biomedical informatics. This course explores methods of use in the design and maintenance of biomedical databases, machine learning techniques, information retrieval in biomedicine and other methods specific to bioinformatics.
330 Topics in Translational Bioinformatics. (3)
The course will cover bioinformatics methods and applications used in the field of translational medicine research. Topics will include bioinformatics data acquisition and management (e.g., microarrays, database modeling and integration), analysis methodologies (e.g., statistics, data mining) and applications.
332 Team Dynamics for Healthcare IT Projects. (3)
This course teaches the fundamentals of leadership, management, and team dynamics in a project-focused software engineering environment, and with a focus on informatics and health care applications. The course will teach students about team and group dynamics, recognizing dysfunctional teams, and helping to fostering productive group and leadership skills.
400 Level Courses
461 Advanced Topics in Biomedical Informatics I. (3)
This course covers current trends and cutting edge research areas of clinical, public health and consumer health informatics. The course has a particular emphasis on research that is of relevance to patients, and the healthy public, covering such topics as outbreak detection and the personal health record. In addition, it will cover research on the use of technology in medical education, and the ways in which clinical decision support systems are applied in contemporary medical practice.
462 Advanced Topics In Biomedical Informatics II. (3)
This course covers current trends and cutting edge research areas of bioinformatics, imaging informatics and translational science. The emphasis of this course is on informatics approaches to the novel data sources that are supplied by the next generation of methods for affordable gene sequencing, and initiatives underway to accelerate the integration of novel research findings into everyday clinical practice. Includes a series on modeling and simulation, current approaches to computer-aided diagnosis of medical images, ways in which technology can support the discovery of new knowledge, and the application of telemedicine to facilitate clinical care remotely.
465 Introduction to Comparative Genomics. (3)
Explores genomic sequences and hypotheses for their structure, evolutionary history, and underlying mechanisms.
466 Bioinformatics and Molecular Evolution. (3)
Fundamental biological motivation and methods used in bioinformatics.
482 Capstone I. (3)
First course in capstone sequence for biomedical informatics majors emphasizing the development of technical skills and effective team work within the context of a research project in biomedical informatics.
483 Capstone II. (3)
Second course in capstone sequence for biomedical informatics majors emphasizing the development of technical skills and effective team work within the context of an applied project in biomedical informatics.
500 Level Courses
500 Research Methods in Biomedical Informatics. (3)
The course presents an introduction to the conduct of scientific research in biomedical informatics: hypothesis formulation, observation and measurement, design of experiments, execution of protocols, development of essential research skills, and sensitivity to ethical issues that arise in research.
501 Introduction to Biomedical Informatics. (3)
Overview of the field of biomedical informatics for use of computers and information in healthcare and the biomedical sciences.
502 Foundations of Biomedical Informatics Methods I. (3)
The first semester of a two semester course surveying the methods and theories underlying the field of biomedical informatics.
505 Foundations of Biomedical Informatics Methods II. (3)
The second semester of a two semester course surveying the methods and theories underlying the field of biomedical informatics.
511 Decision Support and Evidence-Based Practice. (3)
Biomedical decision making and the tools and techniques for decision support, including artificial intelligence and machine learning techniques.
515 Advanced Biostatistical Methods. (3)
Biomedical data, statistical methods and software, basic probability and statistics, confidence interval, hypothesis tests, analysis of variance, binary data.
516 Advanced Biomedical Data Analysis. (3)
Acquisition, conversion and organization of biological data into relevant diagnostic, therapeutic, and research information using information extraction and data mining.
520 Modeling Gene Regulatory Networks. (3)
Computational and mathematical modeling used to approximate gene regulatory networks as well as signaling pathways and inference of model parameters.
540 Problem Solving in Biomedical Informatics. (3)
Theory and practice of software engineering principles as they apply to large- and medium-scale clinical systems from bench to bedside.
541 Cognition and Decision Making in Healthcare. (3)
Conceptual and methodological issues in cognitive science and medical informatics, including design and use of technology in medical settings.
555 Medical Information Management. (3)
Database theory and information management systems with applications to biomedical domain, including use of databases in the Web context.
560 Teaching in Biomedical Informatics. (1)
The student enrolled in the course will serve as a teaching assistant with a faculty member who is teaching either a required or elective course in the Biomedical Informatics curriculum. Such courses will generally be BMI courses but with the approval of the student’s faculty program advisor may include a course that is an approved elective in the BMI program but is offered in another department within the university. The experience is intended to provide the student with a significant teaching role, either lecturing or moderating small group sessions or laboratories. Grading class homework assignments and designing/grading examinations will also typically be part of the experience. Assisting other students outside of class and being available during office hours will also generally be part of the student teacher’s role. A student may also receive 1 hour of credit for participating substantively in the design of a new course, working closely with a faculty member. Generally in such circumstances the student should then serve as course teaching assistant when it is offered for the first time (separate enrollment in this course).
580 Practicum. (1-12)
Structured practical experience in a professional program, supervised by a practitioner and/or faculty member with whom the student works closely.
584 Internship. (1)
Structured practical experience following a contract or plan, supervised by faculty and practitioners.
590 Reading and Conference. (3)
Independent study in which a student meets regularly with a faculty member to discuss assignments.
591 Human Computer Interaction in Biomedicine. (3)
User interface design, development and evaluation for health information systems, medical simulation systems, medical devices, consumer health web sites, and other healthcare related systems.
591 Current Challenges in Molecular Informatics. (3)
The growth in the amount of biological sequence, structure, and function data has led to discipline-changing challenges and broad classes of problems from an information perspective. We will explore the latest approaches to these challenges and discover how molecular biology is evolving from a technique and technology-driven science to an information-driven science. The class takes an active approach to research and is expected to be highly participatory.
591 Introduction to Clinical Environment. (3)
The course is divided into three sections. In the first section we will cover medical and health care concepts and terms, and discuss observational techniques. In the second portion of the course, students will investigate a variety of different clinical environments and report back to the class on their findings. The final portion of the class will summarize the key points of information technology in these clinical environments.
591 Clinical Decision Support. (3)
The course will provide an in-depth analysis of computer-based approaches to supporting clinical decision making for providers and patients (as well as healthy individuals, for disease prevention/health maintenance). The history of the field of clinical decision support (CDS) and the spectrum of methodologies will be reviewed. Practical issues involved in successful implementations of CDS will be discussed. The roles of standards and infrastructure will be examined. Current research topics and foci will be explored.
591 Topics in Translational Bioinformatics. (3)
The course will provide an introduction to bioinformatics methods and applications used in the field of translational medicine research. Topics will include bioinformatics data acquisition and management (e.g., microarrays, sequencers, database integration), analysis methodologies (e.g., dynamic programming, statistics, data mining) and applications. Active student participation will be required through weekly paper reading assignments, presentations and projects.
591 Introduction to Digital Image Processing. (3)
This course is a core course in imaging informatics with the focus on the principles of digital image processing and analysis. The first part of the course introduces the basics of image processing (e.g., point and neighborhood processing, image geometry, Fourier transform, image restoration, image segmentation, mathematical morphology, image topology, shapes and boundaries, color processing, image coding and compression, wavelet, and special effects), and the second part of the course covers advanced topics in image analysis (e.g., K-means and fuzzy C-means clustering, nonlinear diffusion filtering, PDE-based image filtering, mixture modeling, Markov random field-based image segmentation, parametric and geometric deformable models). As an introduction course, it involves minimal mathematics, and is software (matlab) oriented and has a plenty of examples and excises, and highlights applications with images from medicine and biology. This course is specially designed at the request of our BMI students who attended my class in the last semester.
591 Public Health Informatics. (3)
Course covers public health/population health topics including origins, information systems, data collection methods and assessing value.
591 Informatics in Biomedical Imaging. (1)
Biomedical Imaging Informatics is an interdisciplinary field of Biomedical Imaging and Biomedical Informatics at the intersection Information Sciences, Computer Science, Biological Sciences, Medical Sciences and Imaging Sciences (Radiology). It studies how information about and contained within biomedical images is retrieved, analyzed, enhanced, and exchanged within radiology and throughout the health care enterprise, involving all aspects of the biomedical imaging chain.
592 Research. (1-12)
Independent study in which a student, under the supervision of a faculty member, conducts research that is expected to lead to a specific project such as a thesis or dissertation, report, or publication.
595 Continuing Registration. (1)
Used in situations where registration is necessary but where credit is not needed. Replaces arbitrary enrollment in reading and conference, research, thesis, dissertation, etc. Used by students when taking comprehensive examinations, defending theses or dissertations, or fulfilling the continuous enrollment requirement in doctoral programs. Credit is not awarded, and no grade is assigned.
599 Thesis. (1-12)
Supervised research focused on preparation of thesis, including literature review, research, data collection and analysis, and writing.
790 Reading and Conference. (3)
Independent study in which a student meets regularly with a faculty member to discuss assignments.
792 Research. (1-15)
Independent study in which a student, under the supervision of a faculty member, conducts research that is expected to lead to a specific project such as a dissertation, report, or publication.
799 Dissertation. (1-15)
Supervised research focused on preparation of dissertation, including literature review, research, data collection and analysis, and writing.