BMIN

2.00 Informatics

Subject

BMIN

Credits (min)

1

Credits (max)

1

Type

course

Edition

2026-2027

Source

catalog.upenn.edu

2 Biomedical Research

Subject

BMIN

Credits (min)

1

Credits (max)

1

Type

course

Edition

2026-2027

Source

catalog.upenn.edu

2 BMIN 5020 Database and Data Integration in 1

Subject

BMIN

Credits (min)

1

Credits (max)

1

Type

course

Edition

2026-2027

Source

catalog.upenn.edu

This course is designed to provide an in-depth look at topics that are of essential importance in biomedical informatics. Each topic will be arranged into thematic modules which will occur in consecutive weeks in the class schedule, with the intention that each module becomes its own "mini-course". The topics for each module may rotate from semester to semester, based on these criteria: Historical importance to the current field of biomedical informatics research and/or practice; Cutting-edge developments in biomedical informatics; Topics not covered in depth in BMIN 5010; Consensus of the program leadership and teaching faculty. It is recommended that students have completed BMIN 5020 and BMIN 5030 prior to enrolling in this course. NOTE: Non-majors need permission from the instructor.

Subject

BMIN

Type

course

Edition

2026-2027

Source

catalog.upenn.edu

ce, BMIN 5070 Human Factors

Subject

BMIN

Type

course

Edition

2026-2027

Source

catalog.upenn.edu

2.00 BMIN 5120 Human Computer Interaction for 0.5

Subject

BMIN

Credits (min)

0.5

Credits (max)

0.5

Type

course

Edition

2026-2027

Source

catalog.upenn.edu

This course is designed to develop intelligent consumers, managers, and researchers of telehealth and personal health/ consumer health informatics systems through guided exploration into the components of such systems. The course is designed to introduce many of the challenges facing designers and managers of telehealth/ mHealth and remote health care delivery networks. The spectrum of activity ranging from research into implications of system design for applications that bridge geographic distance to the development of practical applications to promote patient engagement is considered in both historical context and in case studies. The current status and future trends of this emerging domain are reviewed. It is recommended that students have some exposure to health care or health systems prior to enrolling in this course. NOTE: Non-majors need permission from the department.

Subject

BMIN

Type

course

Edition

2026-2027

Source

catalog.upenn.edu

Subject

BMIN

Type

course

Edition

2026-2027

Source

catalog.upenn.edu

This course provides an introduction to the concepts and principles of learning health systems, focusing on the roles and methods of biomedical informatics in the data-knowledge-practice learning cycle that is the hallmark of such systems. Topics to be included in the course are history of health systems; information systems analysis and design as these apply to learning health systems; methods for integrating data from heterogeneous sources; analytic methods for establishing evidence and evaluating its usefulness in improving patient outcomes; and, working with teams in the learning health system context. There is a strong emphasis on applying these techniques to real-world issues with clinical and clinical research information systems. These include the electronic health record, information systems in clinical specialties, and systems to support the management of data used for clinical research and healthcare administration. This course is required for all MBMI students. Recommended prerequisite: BMIN 5010 and BMIN 5030

Subject

BMIN

Type

course

Edition

2026-2027

Source

catalog.upenn.edu

The course provides a foundation for practical, hands-on application of human computer interaction in the design and implementation of technology-based healthcare applications. Topics will include: 1. Human Computer Interaction history, key concepts, and relation to human factors; 2. Complex applications and multiple methods for design in applications such as electronic health records, mobile applications, and safety event reporting; 3. Practical application of methods such as problem definition, contextual interview, think-aloud protocol, cognitive walkthrough, and usability testing; 3. Artificial Intelligence in healthcare including of Natural Language Processing (NLP) and Large Language Models (LLMs) as tools for documentation and reducing clinician burnout. Students will be expected to conduct a course-long project applying human computer interaction methodologies to a problem to be chosen using problem definition methods addressed in class, as well as completing all reading assignments and presenting one topic to the class. Pre-requisite (required): BMIN 5070 Human Factors

Subject

BMIN

Type

course

Edition

2026-2027

Source

catalog.upenn.edu

This course explores core principles, theories, and methods from epidemiology, causal inference, biostatistics, and data science, with an emphasis on their application to inform, address, and evaluate health systems-focused research questions and interventions. The ideal learner will have a general familiarity with data analysis, electronic medical records, and experience or planned health systems projects, and is likely a doctoral student, post-doctoral researcher, or faculty member; however, interested students may contact the professor to discuss. The course will cover a wide range of topics to enhance students' familiarity, literacy, and critical appraisal skills in health-syst based randomized trials (including cluster and pragmatic trials), quasi- experimental and observational study designs and methods (such as pre/post studies, differences-in-differences, and time series analysis), as well as general considerations related to multivariable regression modeling, measurement error, missing data, predictive modeling, data integration, and related and emerging topics in learning health system science. Additional topics will vary yearly based on the availability of guest lectures and student composition and needs and may include, for example, lectures on advanced methods (such as Bayesian statistics for clinical research and machine learning), scientific and grant writing, informed consent ,and research ethics. Classes will be centered around instructor-led lectures, journal clubs, student-led presentations, case studies, and expert panels.

Subject

BMIN

Type

course

Edition

2026-2027

Source

catalog.upenn.edu

Recent advances in artificial intelligence (AI) have revolutionized the practice of scientific and biomedical research. AI is often used interchangeably with the term 'machine learning', which itself is only one of the subfields within AI dealing with the broader concept of inductive reasoning. However, a wealth of key prerequisite topics that focus on deductive reasoning are central to the practice of AI in biomedical informatics. These founding principles and their intersection with biomedical informatics are the focus of this first course on artificial intelligence. This course is divided into modules that cover (1) introductory/background materials, (2) knowledge representation, (3) logic, (4) essentials of rule-based systems, (5) search, (6) information structure and inference, and (7) special topics. These topics offer a global foundation for the branches of AI in biomedicine and support a deeper understanding of inductive reasoning and machine learning. More broadly, we will explore how biomedical data can be organized, represented, interpreted, searched, and applied to derive knowledge, make decisions, and ultimately make predictions while avoiding bias. It is expected that students will be familiar with basic biomedical concepts, terminology, and statistics. Additionally, students should be competent in one or more computer programming languages (Python is preferred), and should be familiar with basic programming concepts including data structures, control flow, and I/O. It is recommended, but not required, that students have taken Introduction to Biomedical Informatics (BMIN 5010) and Data Science for Biomedical Informatics (BMIN 5030). No previous exposure to artificial intelligence is assumed.

Subject

BMIN

Type

course

Edition

2026-2027

Source

catalog.upenn.edu

Applications in Machine Learning

Subject

BMIN

Type

course

Edition

2026-2027

Source

catalog.upenn.edu

The growing volume of unstructured health-related data presents unparalleled challenges and opportunities for informaticians, clinicians, epidemiologists and other public health researchers that seek to mine the rich information "locked" within free-texts. Clinical records, social media, published literature, transcribed text, among other textual sources are designed for human eyes, but not necessarily for automatic processing. In this class, we will survey the most recent natural language processing methods used for identifying and classifying information present in these sources. The class provides learning of health language processing – that is, the fundamental principles and methods of both natural language processing and machine learning and how they are currently applied in the biomedical domain. The class will focus on real problems in the context of health research where data are inherently biased, e.g., noisy, missing, or extremely imbalanced. Methods for addressing these biases, such as text normalization, rules-based systems, machine learning (supervised, unsupervised, active learning), deep learning, and large language models will be discussed. In-class lectures will be most often taught using Jupyter notebooks and guest speakers presenting how an NLP/ML method was used to solve a driving biomedical use case. This course requires proficiency in python programming and machine learning. NOTE: Non-majors need permission from the instructor.

Subject

BMIN

Type

course

Edition

2026-2027

Source

catalog.upenn.edu

Spring1 Course Unit

Subject

BMIN

Type

course

Edition

2026-2027

Source

catalog.upenn.edu

This introductory course is designed to provide an overview of the Python programming language including data types, data structures, variables, packages, modules, programming practices, and more. Using lectures and hands-on demonstrations, students will learn how to write Python programs that store, retrieve, represent, transform, analyze, and visualize biomedical and clinical data. Upon completing this introductory course, students will have acquired foundational knowledge using Python to solve problems as well as gained the self-confidence to expand their knowledge of Python well beyond this course. Non-majors need permission from the department.

Subject

BMIN

Type

course

Edition

2026-2027

Source

catalog.upenn.edu

BMIN 5330 is an introductory course in probability theory and statistical inference for graduate students in Genomics and Computational Biology. The goal of the course is to provide foundation of basic concepts and tools as well as hands-on practice in their application to problems in genomics. At the completion of the course, students should have an intuitive understanding of basic probability and statistical inference and be prepared to select and execute appropriate statistical approaches in their future research. Also Offered As: GCB 5330, IMUN 5770 1 Course Unit University of Pennsylvania Catalog 1229

Subject

BMIN

Type

course

Edition

2026-2027

Source

catalog.upenn.edu

The growth and development of electronic health records, genetic information, sensor technologies and computing power propelled health care into the big data era. This course will emphasize data science strategies and techniques for extracting knowledge from structured and unstructured data sources. The course will follow the data science process from obtaining raw data, processing and cleaning, conducting exploratory data analysis, building models and algorithms, communication and visualization, to producing data products. Students will participate in hands-on exercises whenever possible using a clinical dataset.

Subject

BMIN

Type

course

Edition

2026-2027

Source

catalog.upenn.edu

This course is designed to provide an in-depth look at several topics that are of essential or timely importance in biomedical informatics by examining historic and current peer-reviewed literature. Each topic will be allotted three to five consecutive weeks in the class schedule with the intention that each module becomes its own “mini- course”. The course activities will be organized into two segments. In the first section, we will focus on reviewing, presenting, and writing about primary literature. In the second section, we will also expand our writing to include developing new questions and approaches. For PhD students, they will ultimately prepare a short grant proposal using the NIH application format in the second half of the course. At the end of the semester, we will break the class into two “study sections” where students will review each other’s proposals. 1 Course Unit

Subject

BMIN

Type

course

Edition

2026-2027

Source

catalog.upenn.edu

An opportunity for the biomedical informatics student to become closely associated with a professor to develop a program of independent in- depth study in a subject area in which the professor and student have a common interest that is not covered (or covered in depth) in the biomedical informatics program curriculum. The challenge of the task undertaken must be consistent with the student's academic level. To register for this course, the student and supervising professor jointly submit a detailed proposal to the program Curriculum Committee via the Program Coordinator not later than two weeks before the beginning of the semester. This course is open only to students enrolled in one of the approved Biomedical Informatics programs. The course can be taken for 0.5 or 1.0cu, depending on the depth and breadth of the proposed independent study.

Subject

BMIN

Type

course

Edition

2026-2027

Source

catalog.upenn.edu

Total Course Units 10 2026-27 Catalog | Generated 08/03/26

Subject

BMIN

Credits (min)

1

Credits (max)

1

Type

course

Edition

2026-2027

Source

catalog.upenn.edu

Concentration Requirements 2 s Clinical Science Informatics

Subject

BMIN

Type

course

Edition

2026-2027

Source

catalog.upenn.edu