Course Descriptions

This is the core introductory course for students beginning a master’s degree in Biomedical Informatics or for students in other graduate degree programs seeking an introductory overview of the core theories, challenges, research methods and areas for the development of health information management systems and applications.

Building on BMI 501 as a prerequisite, this course surveys the structures and information management challenges of the U.S. health care system, public health system and biomedical research system as well as major other international health care systems. It also surveys other health care informatics application domains that build on or complement electronic health record systems.

This course provides a technical overview of the current computing and information technology systems, programing languages and software development tools available to manage, access and analyze health and biomedical research information effectively in patient care and research settings. Course work includes lectures, demonstrations and readings as well as individual and group hands-on problem exercises with test versions of representative current electronic health record and other health information databases, programming languages and internet/web health information portals.

Focusing on clinical data and research, this course surveys the essential elements of statistical data analysis methods and research strategies that are needed for health and biomedical research information systems and for health information management applications for clinicians and researchers.

This course provides an overview of the methods, systems, tools and databases available for the storage, analysis and interpretation of the increasingly voluminous molecular genome and protein data. The course focuses on the use of these biological data for research in molecular biology, systems biology, genetics and genomics as well as for translating and integrating biological data with clinical health care data to help predict and prevent disease and help clinicians, patients and consumers understand and use this information to maintain health. The course also includes a brief review of current core terminology and concepts in molecular biology, systems biology, genetics and genomics for students without previous course work or training in biomedicine.

Building on BMI 501 as a prerequisite, this course provides an in-depth survey of the data standards, data analysis tools, databases and information management systems and applications associated with clinical population research and the U.S. public health system.

Building on BMI 501 as a prerequisite, this course provides an in-depth exploration of the purpose, scope, technical structures and uses of electronic health records (EHR) and other clinical health care information systems.  Then, building on a review of current research on human cognition and decision making, the course critically reviews the purposes, scope, technical structures and ethical uses of computer-based decision support systems in clinical health care and consumer health settings.

Building on BMI 501 as a prerequisite, this course first provides a review of the theories underlying biomedical knowledge generation and the methods and tools for knowledge acquisition, modeling and representation as well as the management and maintenance of biomedical knowledge sources. The second part of the course provides an in-depth review of current theories and research underlying the development of biomedical ontologies as well as a comparative critical analysis of the major current biomedical ontologies and the methods and tools for biomedical ontology development and evaluation.

Building on BMI 501 as a prerequisite, this course reviews the interdisciplinary theoretical frameworks, design concepts and analytical foci used in human factors engineering and ergonomics for biomedical information systems. These include the physical, cognitive, organizational/social and environmental challenges of human-computer interactions and a range of human factors approaches to systems design and evaluation. The course also looks at the mediating roles of information technology on clinical and research user performance and the potential implications of a range of innovative new design concepts for biomedical information systems.

This course provides an overview of core business concepts for students in Biomedical Informatics. For those students who will enter management roles such as Chief Medical Informatics Officer, an understanding of business processes and methods is essential for professional success. In addition to introducing basic business topics such as accounting and finance, this course will also emphasize leadership skills, including change management and emotional intelligence.

In consultation with his/her faculty advisor, each student may elect to explore a particular area of biomedical informatics in more depth with a member of the BMI faculty with expertise in this area of research or application development. The choice of topic areas will depend, in part, on the availability of the faculty with expertise during the semester the student is seeking this kind of elective. In addition, the number of credit hours and the format of the course will depend on the interests and needs of the student and whether other students are interested in taking an elective on the same topic area. Each special topic elective will include, at a minimum, a program of background, in-depth readings and “laboratory” hands-on work with resources and tools needed in this topic area of BMI research and application development.

In consultation with his/her faculty adviser, each student may also choose, as an elective, to participate in a more advanced research project with a faculty mentor. This could be to learn more about the research methods that will be needed to complete the student’s thesis or, for students planning to continue beyond the master’s degree to a PhD, to explore another possible area of research focus for a doctoral dissertation. The number of credit hours will depend on the interests and needs of each student.

Building on BMI 501, 502, 504 and 506 as prerequisites, this course provides an in-depth survey of the data standards, data analytic methods, data analysis tools, databases and information management systems and applications associated with clinical-genomic population research and the U.S. public health system. Students will learn clinical trial design and data analysis for varying populations. Students will learn genetic epidemiology. Methods will include hierarchical clustering, vector space methods, semantic clustering, machine learning, modeling and simulations (including bootstrap methods). Students will learn linear and non-linear methods of data analysis. Students will be given an introduction to complexity theory and will be shown some methods for reducing dimensionality in complex systems (including computing techniques).

Building on BMI 504 or an equivalent introductory course in biomedical statistics as a prerequisite, this course provides doctoral students with the ability to effectively understand and use a number of key advanced statistical analysis methods and tools used in biomedical informatics research. These include regression and correlation analysis, the analysis of variance and covariance, distribution-free and nonparametric analysis methods and the methods used in demography and vital statistics analysis.

Building on introductory overviews provided in BMI 503 and 504, this course provides an in-depth introduction to the needs, challenges, standards, software applications and tools for biomedical data mining and natural language processing. The most common biomedical data mining methods are reviewed with lab time for using the IBM SPSS Modeler with problem datasets. Similarly, the steps needed to automate the processing and analysis of electronic biomedical text are reviewed, with lab time for using the GATE software package with NLP problem sets. The course concludes with an in-depth review of the unique challenges of processing clinical language and a look at current NLP published research.

Building on the introduction to BMI research methods in BMI 504, this course provides an in-depth review of the methods for conducting effective and unbiased evaluations of health information systems, including economic or cost analysis studies and the challenges associated with these methods. The course includes an exploration of the place of evaluation within the field of biomedical informatics; the major objectivist (quantitative) and subjectivist (qualitative) evaluation study methods; the motivations and methods for economic (cost) analysis as a component of evaluation studies; and the strategies for proposing evaluation studies, communicating their results and dealing with ethical, legal and regulatory issues associated with information systems evaluation.

Building on BMI 505 as a prerequisite, this course provides an in-depth exploration of the purpose, scope, technical structures and uses of electronic health records (EHR) and other clinical health care information systems, in addition to a critical  review of the purposes, scope, technical structures and ethical uses of computer-based decision support systems in clinical health care and consumer health settings. Students will build an expert system and test the system against real, anonymized datasets. Students will generate order sets and computerized physician order entry (CPOE) decision rules. Then, building on BMI 507, this course engages the students to solve ethical dilemmas in the area of clinical decision-making. This course critically reviews the purposes, scope, technical structures and ethical uses of computer-based decision support systems in clinical health care and consumer health settings.

Building on BMI 506 as a prerequisite, this course provides students with hands-on experience with the methods, systems, tools and databases available for the storage, analysis and interpretation of the increasingly voluminous molecular genome and protein data.  The course focuses on exercises that make use of these biological big data for research in molecular biology, systems biology, genetics and genomics as well as for translating and integrating biological data with clinical health care data to help predict and prevent disease. Students will use BLAST (Basic Local Alignment Search Tool), Protein Structure Prediction Software and Galaxy to analyze genomic, epigenetic, gene expression and proteomic data.

Building on BMI 507 as a prerequisite, this course first provides a review of the theories underlying biomedical knowledge representation and ontology. The methods and tools for applied ontology as well as the management and maintenance of biomedical ontologies will be discussed in detail, including the principles of ontological realism and the implementation thereof in the Basic Formal Ontology (BFO). Students will gain experience with the Web Ontology Language (OWL) and the limitations thereof, and with utilities to query ontologies expressed in OWL. The students will learn how to use and evaluate classifiers and their role in subsumption. They will learn both the transitive and reflexive closure of subsumption of a KR system and its applied use in ontology development, maintenance and use. This course also provides an in-depth review of current theories and research underlying the development of biomedical ontologies, a comparative critical analysis of the major current biomedical ontologies as well as the methods and tools for biomedical ontology development, use and evaluation.

Building on BMI 508, this course includes an in-depth exploration of the challenges and opportunities for building effective, integrated information systems to manage and maintain clinical population data for health care outcomes management and research as well as an in-depth view of the core U.S. and international information management challenges and opportunities of public health.

Building on BMI 509 as a prerequisite, this course reviews the interdisciplinary theoretical frameworks, design concepts and analytical foci used in human factors engineering and ergonomics for biomedical information systems. It will also discuss the sociotechnical influences on health information technology (IT) and informatics. These include the physical, cognitive, organizational/social and environmental challenges of human-computer interactions and a range of human factors approaches to systems design and evaluation. The course also looks at the mediating roles of IT on clinical and research user performance and the potential implications of a range of innovative new design concepts for biomedical information systems. In addition to reviewing these concepts with more advanced examples, the students will be exposed to case studies that will allow them to gain problem-solving skills in this important informatics domain.

Repeated for the first four semesters of the PhD program, this course provides each student with experience working in the research labs of two or three members of the BMI faculty, to get a broader perspective on the research challenges of the field and to help the student choose one of the five BMI department divisions as well as the specific research questions for his or her dissertation research. The student’s faculty research mentors and research lab rotations will be chosen in consultation with each student’s initial faculty adviser, based on how the student’s interests and research goals complement those of the department’s faculty.

Repeated for the four semesters of the third and fourth years of the PhD program, this course provides each student with more focused research experience working in the lab or labs of his or her faculty dissertation adviser or other members of his or her dissertation advisory committee. For most students, BMI 711 research work credit will be earned after completing the PhD qualifying exam, however, every student in the PhD program must pass the qualifying exam no later than the end of the first semester of BMI 711 research experience (i.e., the end of the first semester of the third year of the PhD program).

Repeated during the last two semesters (the fifth year) of the PhD program, this course provides time for the PhD candidate to complete his or her dissertation research, write the publishable dissertation thesis and prepare for the defense and formal presentation of her or his research results. During this final phase of the dissertation work, the candidate will continue to consult regularly with the members of his or her dissertation advisory committee.

Building on BMI 506 as a prerequisite and BMI 706 as a highly recommended advanced selective, this course provides an in-depth exploration of the purpose, scope, technical structures and uses of referent tracking as a methodology to design information systems that are self-explanatory in terms of the data they manage and “self-aware” in terms of their interactions with users or other systems. The course includes both theoretical lectures and group discussions, the latter aimed to help integrate all aspects of referent tracking into practical applications the students will design.