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Course Catalog

Analytics for Healthcare - BMI 524/624
Seminar Instructors: Abhijit Pandit, M.B.A., Tracy Edinger, N.D. 
Credits: 3.0
Content: This course is a broad overview of how analytics is used in healthcare settings. While analytics can take many forms we primarily focus on the use of this framework to advance the goals of healthcare organizations. We attempt to cover the process, infrastructure needs, tools, skills, organization, people and governance to effectively perform this work in a healthcare organization. We do not focus on particular quantitative methodologies and therefore our course should be considered 'non-technical'. Instead we provide a broad overview of the field with exposure to several common tools and software packages. This course is designed for CI-major students.
Prerequisites: None
Offering: Online: Spring
 

Instructors: Steven Chamberlin, N.D., William Hersh, M.D.
Credits: 3.0
Content: This course is an overview of the application of data science, machine learning, and artificial intelligence in health care settings. Students will be introduced to a wide range of machine learning topics, including identifying health care issues that can be addressed with machine learning solutions, machine learning model development and data source identification, machine learning model implementation, critical appraisal of machine learning literature, and ethical considerations for the application of machine learning and artificial intelligence in health care. Students will also identify an issue in health and develop their own machine learning model to address this issue.
Prereq: BMI 540 or instructor's permission
Offering: Online, Winter

Bioinformatics and Computational Biology I: Algorithms - BMI 550/650
Instructor: James Jacobs, M.D.
Credits: 4.0
Content: The course will be a problem-driven examination of the algorithmic issues in computational biology. The course will provide students with the computational fundamentals underlying the techniques covered. Students will be expected to learn basic algorithm principles, basic mathematical and statistical proofs, and molecular biology. The emphasis is on algorithm development and application to biological problems, particularly those from functional genomics studies. Topics will include: Mapping (Genetic linkage maps, physical maps), Sequencing (Whole genome sequencing: shotgun approaches and ESTs), Sequence analysis (multiple sequence alignment, fragment assembly, EST assembly, genome annotation, algorithmic side of gene finding and BLAST). Students will be evaluated on written assignments and a programming project.
Prerequisite: Introductory programming course or instructor's permission
Note: 4 credits includes 1 credit Lab
Offering: On Campus: Fall

Bioinformatics and Computational Biology II: Statistical Methods - BMI 551/651
Instructor: James Jacobs, M.D.
Credits: 4.0
Content: This course will be a problem-driven examination of the quantitative issues in computational biology. The course will provide students with the statistical fundamentals underlying the techniques covered. Topics will include applications involving MCMC Models, Maximum Likelihood, Random Walks, Hidden Markov Models, Estimating Genealogical Relationships and Networks. Students will be evaluated on written assignments and a programming project.
Note: 4 credits includes 1 credit Lab
Prerequisite: Non-BMI students require instructor's permission
Offering: On Campus: Winter
 

Bioinformatics Programming and Scripting - BMI 565/665
Instructor: Michael Mooney, Ph.D.
Credits: 3.0
Content: The purpose of this course is to equip research scientists with computational skills necessary to create and automate tools to analyze biological data.  The course is divided into four sub-topics: python programming, scripting in unix, biopython library, bioinformatics workflows.  Python will be used used to solve simple to sophisticated programming problems and to review general computational language paradigms such as problem abstraction, data types, file I/O, iteration, functions, and objects.  There will also be an emphasis on writing unix operating system shell scripts to automate repetitive tasks and connect disparate bioinformatics tools using files and pipes.  In addition, students will learn to access public repositories to perform basic bioinformatics tasks such as annotating gene products, sequence searching, and functional queries. This course is designed to be a first year requirement for students in the Bioinformatics and Computational Biology graduate program in Biomedical Informatics. Open to other students with consent of instructor.
Prerequisite: Background must include an introductory programming class including concepts such as variables, loops, I/O, methods, and algorithms.
Offering: On Campus: Fall
 

Capstone Project- BMI 581
Instructor: Varies
Credits: 6.0
Content: A two to three-term capstone project in biomedical informatics is a non-independent project that will be performed under the close supervision of the capstone advisor. Project possibilities include, but are not limited to: developing a project that fits into a larger framework, systematic review, piece of an ongoing research project, substantial background literature review, assisting in grant writing, curriculum revision, or project with an IT organization, such as ITG. For more information, see the Capstone Project Requirements.
Prerequisite: Consent of instructor, Advisor Assignment Form, proposal outline.
 

Clinical Research Informatics- BMI 523/623
Instructor: Nicole Weiskopf, Ph.D.
Credits: 3.0
Content: This class will introduce the student to the principles of clinical research informatics. Topics include the design of clinical research, clinical trial administration, good clinical data management, clinical trial registration and publication, subject recruitment, use of administrative databases, registries and electronic health records in research, practice-based research networks, standards in terminology and messaging for clinical research, and research collaboration. Requires virtual real time attendance every Monday, 5:00-6:30pm PST.
Prerequisite: None
Offering: Online: Summer/virtual attendance required at synchronous sessions each Monday
 

Computational Genetics -BMI 559/659
Instructor: Shannon McWeeney, Ph.D.
Credits: 3.0
Content: This course will cover the foundation and principles of molecular genetics and population genetics along with the corresponding techniques used to study them. Emphasis will be given to the biological interpretation of the data types that result from current experimental methods.
Prerequisite: Open to admitted BMI students or permission of the instructor
Offering: Online: Fall
 

Computer Science and Programming for Clinical Informatics- BMI 540/640
Instructor: Lorne Walker, M.D., Ph.D.
Credits: 3.0
Content: An introduction to computer science focusing on data science—the representation and storage of data, computer architecture, algorithms, and Python programming with applications to clinical informatics problems and data. Students must demonstrate knowledge of basic structured programming techniques for admission to the class. Prerequisite: Prior college-level computer programming course and successful completion of a pretest.
Offering: Online: Fall
Note: Enrollment limited to admitted Biomedical Informatics students who have completed the introduction to computer programming prerequisite. 
 

Consumer Health Informatics- BMI 520/620
Instructor: Joanne Valerius, Ph.D., M.P.H., R.H.I.A.
Credits: 3.0
Content: This course focuses on the intersection between consumers, information technologies and health care. We explore the design, use and impact of emerging technologies that aim to engage consumers to participate in their health and health care. Concepts discussed arise from various informatics disciplines as well as health communication, behavioral science, quality improvement, psychology and public health. We will review trends, opportunities and challenges in consumer-facing health information technology, taking the perspective of various stakeholders. Topics include: U.S. trends; information quality; access and usage; clinical integration; online peers; and prevention and chronic illness care.
Prerequisites: None
Note: Enrollment limited to admitted Biomedical Informatics students. Non-BMI students need instructor and department permission
Offering: Online: TBD
 

Data Analytics –BMI 569/669
Instructors: TBD
Credits: 3
Content: Data Analytics is an applied hybrid course that introduces the concepts of the data analytics life cycle through the implementation of a quality metric. Through this implementation, we explore the role of analysts and analytics in healthcare organizations. This hybrid course will consist of six weeks of directed readings with online discussions, hands-on use of analytical tools for data extraction, data cleaning and analysis and an on-campus portion. The on-campus portion will consist of lectures, guest speakers, and hands-on lab sessions in R and SQL. The course is required for BCB-track students and CI-track students who wish to increase their knowledge of implementation and practice.
Prerequisites: (BMI 540 or 565) and (BSTA 525 or BSTA 511 or MATH 530). Experience with R (or other scripted statistical language), SQL, and spreadsheets a positive. Students must bring a laptop to class.
Offering: TBD
 

Databases- BMI 544/644
Instructor: Michael Lieberman, MD, MS, FACP, FAMIA
Credits: 3.0 credits.
Content: An in-depth look at databases and database management systems. Topics covered will include data modeling, hierarchical and relational databases, query languages (SQL), database optimization, and OLAP and data warehousing.
Prerequisite: No prerequisite, but prior completion of BMI 540 suggested.
Offering: Online: Winter
Note: Enrollment limited to admitted Biomedical Informatics students.

Deep Learning I - BMI 539A/639A
Instructor: Meysam Asgari, Ph.D., 3.0 credits
Content: Deep neural networks (DNNs) have recently demonstrated superiority to other machine learning techniques in a variety of tasks ranging from speech recognition and natural language processing to computer vision. This course covers a number of topics in machine learning with a specific focus on deep neural networks (DNNs) including model capacity, regularization, overview of optimization techniques, perceptron algorithm and multi-layer perceptron, feed-forward neural networks, convolutional networks, and sequence-to-sequence models. The topics are purposely chosen to cover all the background material that students need to effectively train DNNs through supervised techniques in their research problems. The course will also draw from applications in speech and language processing. Recommended background of this course includes programming proficiency in Python or Matlab, enough knowledge of calculus, linear algebra and probability theory. 
Prerequisite: BMI 543/643 Machine Learning, linear algebra, multivariable calculus, probability
Offering: Fall
 

Deep Learning II - BMI 539B/639B
Instructor: Meysam Asgari, Ph.D.
Credits: 3.0 credits
Content: This course will cover two areas of deep learning in which labeled data is not required: Deep Generative Models and Unsupervised Learning. Recent advances in generative models have made it possible to realistically model high-dimensional raw data such as natural images, audio waveforms and text corpora. Topics include energy-based models (e.g., restricted Boltzmann machines), autoencoders, variational autoencoders, generative adversarial networks, in addition to a brief overview on elements of Bayesian inference including Monte Carlo techniques (e.g., Gibbs sampling and Metropolis-Hasting) and variational inference. This course will cover the theoretical foundations of these topics as well as their newly enabled applications. Students will learn how to effectively train deep models through unsupervised techniques, and will enable them to employ deep models in their research problems. The course will also draw from applications in speech and language processing. This is a second course in the sequence of “deep learning” topics and only those who have previously taken the “Deep Learning I” are encouraged to take this class.
Prerequisite: Deep Learning I
Offering: Winter
 

Design and Evaluation in Health Informatics- BMI 560/660
Instructor: Vishnu Mohan, M.D., M.B.I., FACP, FAMIA
Credits: 3.0
Content: Research and development projects in the broad field of biomedical informatics can take many forms, from field studies that improve understanding of the tasks and information needs of users, to development projects that design, build, and deploy information systems, to studies that assess the impact of information systems on health care processes and outcomes. This course provides an overview of the concepts, vocabularies, and strategies needed to design and evaluate projects in biomedical informatics, including a breadth of methodologies drawn from qualitative research, quantitative research, and software engineering. This is a required course in the MS and PhD HCIN and is recommended for students in their first year of the program. It is an elective in the graduate certificate.
Prerequisite: BSTA 525 (formerly PHPM 524) or BSTA 511/611, and working knowledge of Excel or consent of instructor.
Offering: Online: Winter
 

Electronic Health Record Laboratory Course- BMI 513
Instructor: Vishnu Mohan, M.D.
Credits: 3.0
Content: This course introduces the student to an electronic health record and its functionality by offering a practical, hands-on experience with a modern EHR, with a focus on workflows and system functionality.
This course examines the use of the EHR in both inpatient and outpatient clinical environments, and focuses on specific elements of the EHR including administrative and clinical tools, and also those specific to the role of clinical informaticians, including order set development, customization, clinical decision support, ancillary services such as the clinical laboratory and radiology, and billing and coding.
This is an introduction-level course, designed to offer the most benefit to those who do not have prior exposure to EHRs.
Prerequisite: None
Offering: TBD
 

Evidence-Based Medicine - BMI 536/636
Instructor: William Hersh, M.D.
Credits: 3.0
Content: This hybrid course provides a rigorous introduction to the principles of evidence-based medicine (EBM). It begins with an overview of how to frame an answerable clinical question and then find the best evidence to answer it. The major categories of questions that arise in clinical practice - treatment, diagnosis, harm (etiology), and prognosis - are each covered, with instruction on what is the best type of evidence to answer questions, how to find that evidence, and how to apply it to a given patient. This is followed by units on summarizing evidence (e.g., through systematic reviews and meta-analysis), putting evidence into practice (e.g., implementing clinical practice guidelines), and the limitations of the EBM approach. Some pre-campus coursework is required. See syllabus for details.
Prerequisite: BMI 510/610
Offering: Hybrid: Summer (odd years only)

Healthcare Quality - BMI 537/637
Instructor: Michael Lieberman, MD, MS, FACP, FAMIA
Credits: 3.0
Content: This course covers methods for measuring, managing and improving the quality of health care. A general overview of the health care system in the United States and beyond is followed by the quality challenges and issues in these systems. Students are also taught the principles of quality improvement and are expected to be able to apply them in practical settings. Current national efforts in performance measures, financial incentives and quality are also covered.
Prerequisite: None
Offering: Online: Spring

Human Computer Interaction in Biomedicine - BMI 548/648
Instructor: Michele Hribar, Ph.D.
Credits: 3.0
Content: This hybrid course will provide an overview of the principles and tools of HCI design and evaluation techniques. It will begin with 6-8 weeks of directed readings with small assignments or quizzes followed by one week on campus and then completion of a project. The on-campus portion of the course will have lectures in the morning and lab sessions in the afternoon, for 5 days. Topics to be covered include: Principles of good interface design, The iterative process of design, Surveying techniques, Discount usability testing, Cognitive processes affecting usability, Think-aloud protocols, Physiological processes that affect usability, Eye-tracking techniques, Quantitative evaluative measures, and Research topics in HCI. Some pre-campus coursework is required.
Prerequisite: None
Offering: Hybrid: Summer - Virtual Course/Synchronous sessions Sept 7-11, 9:00 AM - 4:00 PM (PDT).  Requires virtual real-time attendance.

Independent Study- BMI 502/602
Instructor: Varies
Credits: Varies
Prerequisite: Completed 50X/60X form

Information Retrieval- BMI 514/614
Instructor: William Hersh, M.D.
Credits: 3.0
Content: An introduction to text information retrieval in health and biomedicine. After an introduction of models and knowledge-based information, the course covers state-of-the-art approaches to on-line content, indexing, retrieval and evaluation methods. The course then continues with research topics in information retrieval, including automated indexing and retrieval, user interfaces and digital libraries.
Prerequisite: BMI 510/610.
Offering: TBD

Introduction to Biomedical and Health Informatics- BMI 510/610
Instructor: William Hersh, M.D.
Credits: 3.0
Content:  An introduction to the fundamental principles of biomedical and health informatics, the field concerned with the acquisition, storage, and use of data and information in biomedicine and health. The course begins with a basic overview of the field, its terminology, and its resources. It then surveys electronic and personal health records, standards and interoperability, and artificial intelligence, including machine learning, large language models, and their applications in biomedical and health. The course also covers privacy and security, information retrieval, translational bioinformatics, public health informatics, nursing informatics, and consumer health informatics.
Prerequisite: None.
Offering: Online: Fall, Winter, Spring, Summer

Introduction to Programming
Instructor: Lisa Karstens, Ph.D.
Credits: 0.0
Content: This non-credit, online course will introduce the beginning programmer to programming structure and design, creating a solid foundation for all types of programming. The emphasis will be on procedural programming and control structures, although exercises will be in Python. The course fulfills the prerequisite for BMI 540 and BMI 565. Fee is $500.
Offering: Summer
Students may register during the regular summer term registration period. Contact Vanessa Reeves for details at reevesva@ohsu.edu.
 

Machine Learning - BMI 543/643
Instructor: Xubo Song, Ph.D.
Credits: 3.0
Content: This course aims to provide theoretical foundations and practical experience in machine learning. It will give an overview of many models and algorithms used in modern machine learning, including linear models, multi-layer neural networks, support vector machines, density estimation methods, graphical models, mixture models, clustering,  ensemble methods, and convolutional neural networks. The course will give the student the intuition as well as a more formal understanding of how and why these methods work. Students will have an opportunity to experiment with these techniques and apply them a selected problem in the context of a term project.
Prerequisites: Linear algebra, multivariable calculus, probability, programming language, BMI 531.
Offering: Spring
 

Management and Processing of Large Scale Data –BMI 535/635
Instructors: Michael Mooney, Ph.D.
Credits: 3.0
Content: The goal of this course is to provide an introduction to the data management and data processing applications available for large scale data. Utilizing samples from the 1000 Genomes Project, this course will provide hands-on experience managing and processing large scale data. Topics covered include SQL, NoSQL, distributed file systems and parallel computing.
Prerequisite: BMI 565/665, BMI 550/650, familiarity with Linux
Offering: On campus, Winter

Managing Ethics in Biomedical Informatics - BMI 576/676
Instructor: Joanne Valerius, M.P.H., R.H.I.A., Ph.D.
Credits: 3.0
Content: The goal of this course is to introduce and sensitize students to the ethical, legal, and social issues arising in the electronic uses of data focusing on research in health informatics. Students will become familiar with managing and implementing legal and regulatory requirements mandated by HIPAA and other rules or laws when needed. Topics will include critical thinking in ethical decision-making; federal rules and regulations in health informatics research, e. g., HIPAA privacy and security rules; ethical issues in genomics, authorship and whistle blowing, funding research, use of informatics technology to inform public health issues, data provenance and data sharing, diversity and discrimination in health care research, and international research, e.g. GDRP. At the end of this course, students will be able to: Perform and communicate ethical decision-making by applying critical thinking in biomedical informatics research issues; identify sources to inform ethical decision-making and change management when needed. Prerequisite: None
Offering: Online: Spring

Medical Decision Making - BMI 538/638
Instructor: Karen Eden, Ph.D.
Credits: 3.0
Content: This course introduces the student to decision analysis (modeling of decisions). Given uncertain information and limited resources, students will learn to model uncertainty and expected outcomes of various decisions. Course will cover Bayesian theory, decision trees, patient utilities, quality of life and cost related to health outcomes. Students will apply decision analysis techniques in addressing real world problems using software (by TreeAge, Inc.) and participate in online discussion of decision analyses in the medical literature.
Prerequisite: Ability to complete basic algebra problems and knowledge of probability are necessary for this course. If you have questions, please email the instructor, Karen Eden, edenk@ohsu.edu.
Offering: Online: Winter
 

Mentored Teaching Prep- BMI 654
Instructor: Varies
Credits: 1.0
Content: Development of contract with mentor for teaching experience. Decisions will be made regarding lectures, deadlines, scope and topics to be covered. Prepare lesson plans, course materials with Mentor (syllabus, calendar, lectures).
Prerequisite: Doctoral student status

Mentored Teaching- BMI 655
Instructor: Varies
Credits: 3.0
Content: Students teach a subject area course under the mentorship of a faculty member.
Prerequisite: Doctoral student status, BMI 654

Network Science and Biology- BMI 567/667
Instructor: Guanming Wu, Ph.D.
Credits: 3.0
Content: Networks are everywhere: the Internet, social networks, epidemiological networks, protein-protein interaction networks, gene regulatory networks, etc. This course will introduce students to basic concepts shared by many different kinds of networks, with focus on biological networks as examples. Students will learn how to program against networks, search for patterns hidden in networks, and visualize networks generated from real biological data sets. Prerequisite: Background in linear algebra and calculus and knowledge in statistics are expected. Some programming experience using an object-oriented programming language will be needed in order to complete the course projects.
Offering: On campus: Winter (even years only)
 

Organizational Behavior and Management- BMI 517/617
Instructor: Chris Hoekstra, Ph.D.
Credits: 3.0
Content: The most important functions of managers in an organization include understanding and motivating individuals and organizing structural systems within which they can work in a productive manner. This course will review the concepts, issues and practices of organizational behavior at the individual, group and organizational levels. Students will practice applying these concepts in simulated situations to improve personal effectiveness in groups or organizations. At the individual level, topics will include perception, decision-making, values, attitudes, job satisfaction, and motivation. The group level topics are work teams, communication, leadership, power and politics, conflict and negotiation. Organizational level topics include organizational structure, work design, human resources policies, organizational culture and change. 
Prerequisite: None.
Offering: Online: Fall, Spring.

Practice of Health Care - BMI 530/630
Instructor: Craig S. McDougall, M.D.
Credits: 3.0
Content: This course introduces the biomedical informatics student to clinical practice, including the underlying biology and manifestations of selected disease states, the information gathering and reasoning processes used to detect, understand and treat diseases, the health professionals who provide and support care and the clinical settings in which care occurs. In addition to class time, students will observe clinicians caring for patients in real world settings. Clinicians are exempt from taking this class. Students in the master's and PhD programs (Medical Informatics track) will need to substitute a different 3-credit class in its place.
Prerequisite: Offsite students must have a laptop with a webcam, an internet connection and a telephone.
Offering: Online: Fall. All students are required to participate in a weekly synchronous online discussion via Sakai Thursday 5:15-7:15 PST. All students are required to participate remotely for 8 or more sessions.
 

Practicum: Biomedical Informatics- BMI 509/609
Instructor: Varies
Credits: 3.0
Content: A practical hands-on experience in an operational biomedical informatics setting at OHSU, a local health software vendor, or a hospital/health system.
Prerequisite: 50X Form, Consent of Internship Coordinator.

Principles and Practice of Data Visualization - BMI 525/625
Instructor: Steven Bedrick, Ph.D.
Credits: 3.0
Content: This course will give students a foundation in the principles of data visualization, particularly as applied to scientific and technical data, as well as provide students with hands-on experience using modern software tools for developing visualizations. Lecture topics will include an overview of visual perception, color theory and practice, different types of graphs and their purposes, visualizations for specialized forms of data including time-series and geospatial data sets, strategies for working with multidimensional data, etc. There will also be lecture content on ethical issues surrounding data visualization. Weekly lab sessions will introduce students to popular data visualization tools such as R’s ggplot and Shiny, etc.
Prerequisite: Ready for R

Offering:  On Campus: Spring
 

Probability and Statistical Inference-BMI 531/631
Instructor: Steven Chamberlin, N.D., M.S.
Credits: 3
Content: This course will introduce fundamental concepts underlying statistical data visualization, analysis, inference and statistical decision making.  The topics include presentation and description of data, basic concepts of probability, Bayes theorem, discrete and continuous probability distributions, estimation, sampling distributions, classical tests of hypotheses on means, variances and proportions, maximum likelihood estimation, Bayesian inference and estimation, linear models, examples of nonlinear models and introduction to simple experimental designs. One of the key notions underlying this course is the role of mathematical modeling in science and engineering with a particular focus on the need for understanding of variability and uncertainty. Examples are chosen from a wide range of bioinformatics and computational biology topics.
Prerequisites: The most important prerequisite for this course is familiarity with multivariate calculus, and some linear algebra and matrix operations. Some homework will also require a computer and familiarity with R. It is recommended to take a class in R, such as R Bootcamp or Ready for R offered from the Department of Medical Informatics and Clinical Epidemiology.
Offering:  Fall: Online
 

Project Management in Informatics- BMI 518/618
Instructor: James McCormack, PhD, MT(ASCP)
Credits: 3.0
Content: This course introduces informatics students to the profession and practice of traditional project management. It exposes learners to the concepts, tools, and techniques project managers use to define, plan, control, and close projects, with attention given to the development of both individual and team skills.
Prerequisite: None.
Offering: Online: Winter
 

Public Health Informatics- BMI 521/621
Instructor: TBD
Credits: 3.0
Content: Recent events underscore the need for a strong public health information infrastructure. Public Health Informatics is the study of how public health information is generated, collected, transferred, and shared. This course is designed to introduce both biomedical informatics and public health students to public health informatics. Course topics will include the information needs of public health professionals; barriers and requirements of a public health information infrastructure; data and process standards; electronic health records; electronic data exchange, including security issues; data registries and sources; evidence-based public health and community health assessment; public health informatics tools, such as GIS; public health reporting and surveillance, including communicable disease, environmental, syndromic, and bioterrorism surveillance.
Prerequisite: None
Offering: TBD
 

Qualitative Research Methods- BMI 561/661
Instructor: Chris Hoekstra, Ph.D.
Credits: 3.0
Content: Qualitative research methods are used to address why, what, how questions and often triangulated with quantitative methods. This hybrid course will be useful for students taking Organizational Behavior and Management in Informatics when writing their cases. Informaticians who evaluate or conduct research within organizations will find this course a useful foundation when considering data gathering options. Required reading prior to first on-campus session: Crabtree, Miller textbook (see textbook list for details).
Prerequisite: BMI 510/610
Note: Enrollment limited to admitted Biomedical Informatics students.
Offering: Virtual Hybrid: Summer, even years only.  See syllabus for specific virtual participation dates and times (PDT).
Syllabus

Quantitative Research Methods- BMI 562/662
Instructor: TBD
Credits: 3.0
Content: The aim of this course is to help students apply the knowledge gained in previous biostatistics courses to quantitative research problems in biomedical informatics. Students are expected to have a sound understanding of basic techniques in biostatistics including descriptive statistics, t-tests, chi-squared, ANOVA, use of contingency tables, correlation and regression analysis, non-parametric methods and modeling. Each week, we will begin with a research question from the informatics literature (or a student's own research). We will identify appropriate research designs, generate hypotheses (if appropriate), select appropriate test statistics, and use software to analyze the data. At the end of the term, we will have covered ten of the most commonly used statistical techniques in the recent informatics literature.
Upon completion of this course, students should be able to:

1. Generate study designs and hypothesis for common research questions in medical informatics
2. Given a research question and a data set, analyze the data and provide results
3. Provide an interpretation of data analysis that is easily understood by a non-statistician.
4. Critique (and suggest alternatives to) study designs and analyses found in the informatics literature.

Prerequisite: (BSTA 525 or Biostats I & II) and BMI 560 Or consent of instructor.
Offering: TBD
 

R Boot-camp
Credits: 0.0
Content: This non-credit, self-directed course is intended to introduce the novice programmer to the basics of R programming. The emphasis of this course is on useful R programming skills including loading and manipulating data within R, and secondarily simple visualization, data QC, and statistics. The structure of this course is meant to introduce you to data structures in R such as vectors, lists, matrices, and data frames along with immediate tasks to learn how they work and how to manipulate them. This course is self-paced. You may begin it at any time and proceed at any pace as long as you complete modules in order. For more sophisticated programmers, each module contains a self-evaluation problem and quiz in order to assess whether you need to complete a module or not. Students may register at any time, class is open to everyone at no charge.
Link to R-bootcamp: https://rstudio.cloud/ to create an account. Then http://r-bootcamp.netlify.com.

Reading and Conference - BMI 505/605
Instructor: Varies
Credits: 1.0-6.0
Content: Students are assigned readings, the meet with instructor to discuss.

Reading and Conference: PhD/Postdoctoral Meeting - BMI 505F/605F
Instructor: Varies
Credits: 1.0
Content: This is a journal club style course in which students are required to present a key paper or research method in their particular field of research. The course is designed to allow the Biomedical Informatics (BMI) PhD students, postdoctoral and clinical fellows to participate in respectful, scholarly discourse in a safe academic learning environment.
Prerequisites: Restricted to BMI PhD students, postdoctoral and clinical fellows. Signature required.
 

Readings in Bioinformatics and Computational Biology - BMI 553/653
Instructor: James Jacobs, M.D.
Credits: 1.0
Content: This is a seminar style course requiring significant student participation that addresses new and emerging technologies and/or methodologies.
Prerequisites: 550 and 551
Offering: On Campus: Spring
 

Ready for R
Credits: 0
Content: This course is meant to be a gentle introduction to using R/Rstudio in your daily work. It aims to teach useful skills (visualization, data loading, data filtering and manipulation, simple statistics) that students can immediately use in their work. No prerequisites or previous experience required. It is not meant to be a substitute for a full programming course or a full course in statistics. In the end, students will apply these skills to a final project. To gain access to course materials, go to https://ready4r.netlify.app/mailing. This course is a prerequisite for BSTA 525 Intro to Biostatistics and BMI 569 Data Analytics. 
Prerequisite: None
Offering: Students may begin this self-directed course at any time.

Research in Bioinformatics and Computational Biology-BMI 552A/652A and BMI 552B/652B
Instructor: Eilis Boudreau, M.D., Ph.D.
Credits: 2.0 over 2 quarters
Content: Winter term will focus on developing an independent idea, delving into the lineage of ideas and concepts, managing a bibliography, the basics of project management, introduction to preparing a grant and career development strategies. Spring term will consist primarily of developing research projects. Required of all PhD students; required of all BCB students (PhD and masters).
Prerequisite: None
Offering: On Campus: Winter (1 cr.) and Spring (1 cr.)

Scientific Writing and Communication for Informatics Students - BMI 570/670
Instructor: Kathryn Pyle, A.M.L.S.
Credits: 3.0
Content: The focus of this course is technical writing and communication. Students will draft abstracts and papers; write for their courses and theses; and learn about writing for publication in scientific journals and grant proposals. Topics also include bibliographic database searching and presentations and posters for scientific meetings. Students will complete and present both individual and group writing projects. Students are encouraged to take this course early in their programs prior to beginning their capstones, theses or dissertations.
Prerequisite: None
Note: Enrollment limited to 20 admitted Biomedical Informatics students. Course offered on a Pass/Fail basis. Registration priority given to master's and PhD students.
Offering: Online: Fall
 

Seminar - BMI 507/607
Instructor: Varies
Credits: 1.0 - 4.0
Content: Special topics in biomedical informatics organized by a group of students or faculty.
Prerequisites: Varies
Note: Course offered on a Pass/No Pass basis.
Offering: TBA
 

Software Engineering - BMI 546/646
Instructor: Aaron Cohen, M.D.
Credits: 3.0
Content: This course covers the basic principles of software engineering geared towards providing students with a solid understanding of the process of producing quality software systems on time and on budget. The main activities in software process models are covered in detail, including: proposal creation, requirements gathering and specification, architecture design, software development methodologies, verification and testing, quality management and maintenance. Students will be expected to demonstrate their mastery of the material by the creation of written documentation for several of these main activities on a hypothetical software project of their choice, as well as by answering homework questions based on assigned reading and passing written exams.
Prerequisite: BMI 540/640 or BMI 565/665, OR consent of instructor.
Offering: Online and On-campus: Spring. 
 

Standards for Interoperability - BMI 516/616
Instructor: Ben Orwoll, M.D.
Credits: 3.0
Content: This course will explore the details of healthcare information technology (HIT) interoperability and standards. The evolution of technology in healthcare, along with the impact on clinical information systems, will be studied. The benefits of integrating healthcare information systems will be investigated, as will the challenges of integrating systems across disparate organizations, healthcare disciplines, and technologies. The value proposition of a standards-based approach to integration will be presented. Students will learn the process of HIT integration projects, and how that parallels the development process of interoperability standards. The course will present an in depth look at standards critical to HIT interoperability – HL7 v2, HL7 v3 RIM, CDA, and SNOMED – and at the use of those standards in national regulations and industry-wide efforts such as IHE. Students will gain experience in navigating through standards documents and tools. Students will utilize the skills and knowledge gained to design a standards-based interoperability project addressing a real-world need.
Prerequisite: BMI 510 or instructor's permission
Offering: Online: Winter
 

Symposium - BMI 657
Instructor: Varies
Credits: 3.0
Content: State-of-the-art literature synthesis in an area of research from which the student will be questioned and graded during a student symposium. Student symposia will be scheduled during several weeks during the quarter and each student presentation can last no longer than 20 minutes.
Prerequisite: Doctoral student status

Thesis/Dissertation - BMI 503/603
Instructor: Varies

Note: The information included in this catalog was accurate at the time of publication. Information described in this catalog may change without notice. Not all courses are offered each academic year and faculty assignments may change.