printer friendly pageBIOMEDIN 388: Stakeholder Competencies for Artificial Intelligence in Healthcare (BIODS 388)
Advancements of machine learning and AI into all areas of medicine are now a reality and they hold the potential to transform healthcare and open up a world of incredible promise for everyone. But we will never realize the potential for these technologies unless all stakeholders have basic competencies in both healthcare and machine learning concepts and principles - this will allow successful, responsible development and deployment of these systems into the healthcare domain. The focus of this course is on the key concepts and principles rather than programming or engineering implementation. Those with backgrounds in healthcare, health policy, healthcare system leadership, pharmaceutical, and clinicians as well as those with data science backgrounds who are new to healthcare applications will be empowered with the knowledge to responsibly and ethically evaluate, critically review, and even use these technologies in healthcare. We will cover machine learning approaches, medical use cases in depth, unique metrics to healthcare, important challenges and pitfalls, and best practices for designing, building, and evaluating machine learning in healthcare applications.
Last offered: Autumn 2020
BIOPHYS 279: Computational Biology: Structure and Organization of Biomolecules and Cells (BIOE 279, BIOMEDIN 279, CME 279, CS 279)
Computational techniques for investigating and designing the three-dimensional structure and dynamics of biomolecules and cells. These computational methods play an increasingly important role in drug discovery, medicine, bioengineering, and molecular biology. Course topics include protein structure prediction, protein design, drug screening, molecular simulation, cellular-level simulation, image analysis for microscopy, and methods for solving structures from crystallography and electron microscopy data. Prerequisites: elementary programming background (
CS 106A or equivalent) and an introductory course in biology or biochemistry.
Terms: Aut
| Units: 3
Instructors:
Dror, R. (PI)
;
Bresette, L. (TA)
;
Li, D. (TA)
;
McAvity, J. (TA)
;
Sayana, R. (TA)
;
Suriana, P. (TA)
;
Xu, J. (TA)
BIOPHYS 371: Computational Biology in Four Dimensions (BIOMEDIN 371, CME 371, CS 371)
Cutting-edge research on computational techniques for investigating and designing the three-dimensional structure and dynamics of biomolecules, cells, and everything in between. These techniques, which draw on approaches ranging from physics-based simulation to machine learning, play an increasingly important role in drug discovery, medicine, bioengineering, and molecular biology. Course is devoted primarily to reading, presentation, discussion, and critique of papers describing important recent research developments. Prerequisite:
CS 106A or equivalent, and an introductory course in biology or biochemistry. Recommended: some experience in mathematical modeling (does not need to be a formal course).
Last offered: Winter 2023
BIOS 203: Market Design and Field Experiments for Health Policy and Medicine
This course will provide the student with the necessary tools to be an avid consumer and user, and potentially a producer, of the market design and field experimental literature (recognized by 4 recent Nobel Prizes in Economics: 2007/2012/2019/2020). In the first part, we introduce use of economic theory and analysis to design allocation mechanisms and market institutions, examples include medical resident matching and kidney exchanges. In the second part, it will provide a summary of recent experimental techniques deployed for both research and practice in economics, health/public policy and tech, and detail how to practically gather and analyze data using experimental methods. Emphasis on connecting to practical applications.
Last offered: Spring 2022
BIOS 285: Rodent Animal Models: Selection, Detection, Dissection, Inspection
This 2-week mini-course will discuss pragmatic approaches to rodent utilization with the aim of empowering graduate students across multiple disciplines to maximize rodent-derived data and minimize the redundant use of animals in biomedical research. Topics will include an introduction to clinical models, practical aspects of rodent blood collection and interpretation, algorithmic approaches to tissue collection for research applications, and an introduction to rodent histopathology, immunohistochemistry, and immunofluorescence. Course instructors include board-certified laboratory animal medicine clinicians and comparative pathologists that are expert h these topics. This course is open to graduate students with or without prior rodent experience.
Last offered: Spring 2022
BIOS 297: COVID-19 Pandemic: Lessons Learned
The Covid-19 pandemic has created unprecedented challenges for individuals, society, medicine and science. The SARS-Cov-2 virus rapidly disseminated since first reports from China on December 31, 2019 and by March 11, 2020 it was declared a global pandemicby the World Health Organization. This course will cover various aspects of Covid-19 including clinical perspectives, public health response, impact of disease modeling, and results of clinical trials and research efforts. As the pandemic evolves the course will discuss the most current data and reflect on successes and ongoing challenges as the world grapples with a pandemic of unmatched proportions.
Last offered: Spring 2021
BIOS 298: Cinematic Discoveries: A movie-based exploration of research rigor, communication and diversity
Through movie depictions of the vaccine discoveries leading to the first Nobel prizes in medicine, the infamous Tuskegee Study, the first heart surgery for Tetralogy of Fallot, the encephalitis lethargica pandemic, and modern oncology trials, the course will explore interdisciplinary work in biomedical sciences, research rigor, consent, stigma and discrimination, researchers¿ and health professionals¿ communication skills, and fundamentals of cinematography. The course will include a lecture, a movie projection and discussion each day for 5 days.
Terms: Aut
| Units: 1
BIOS 407: Essentials of Deep Learning in Medicine
This course delves into the fundamental principles of Deep Learning within the medical field, designed to offer a thorough yet accessible introduction to how these advanced models function, are developed, and are currently transforming healthcare practices. The curriculum covers key areas including neural network architecture, computer vision, natural language processing, convolutional neural networks, alongside classification and regression techniques, aiming to provide students with a solid foundation and intuitive insight into the workings of deep learning applications in medicine.In addition to the core content, participants will have the opportunity to engage with expert-led discussions on the latest advancements and future directions at the intersection of artificial intelligence and medicine.
Terms: Spr, Sum
| Units: 1
Instructors:
Langlotz, C. (PI)
;
Tanner, J. (PI)
BIOS 410: Health Innovations for Equity: The basics of design and innovation to create impact
If you?re looking to use your Biosciences knowledge or interests in engineering and medicine to create health innovations that solve some of the world?s most pressing health problems. This course will give you the tools and skills you need to start this process. The course will focus on the basics of user research, design and prototyping for innovations that can have an impact on health equity outcomes. We will take an interdisciplinary approach to solving these problems, and discuss how to build collaborative and inclusive partnerships for health innovations
Terms: Spr
| Units: 1
Instructors:
Brown, C. (PI)
BIOS 414: Understanding Arthritis Research - Current Approaches and Opportunities
A potentially ?inflammatory? claim: arthritis research deserves no less attention than heart disease or cancer - more than 100 different disorders are encompassed by the term arthritis, affecting nearly a quarter of the U.S. population. In order to improve diagnosis and treatment, current research is highly interdisciplinary in nature, from the choice and design of disease models, to the experimental approaches and analyses applied. In this course, we will cover research approaches from basic sciences to translational and clinical work encompassing Genetics, Immunology, Regenerative Medicine, Data Science and Bioengineering. The structure of the course will involve a brief review of each discipline?s contribution to the field with references to key studies, followed by small group discussions on major landmark studies. The aim of the course is to bring participants up to par with the current state of arthritis research - enabling the audience to identify gaps in the current knowledge, frame fundamental research questions, and design experiments using approaches covered in the class.
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