printer friendly pageBIOC 200: Applied Biochemistry
Enrollment limited to MD candidates. Fundamental concepts of biochemistry as applied to clinical medicine. Topics include vitamins and cofactors, metabolism of carbohydrates, lipids, amino acids and nucleotides, and the integration of metabolic pathways. Clinical case studies discussed in small-group, problem-based learning sessions.
Terms: Aut
| Units: 2
Instructors:
Cowan, T. (PI)
;
Harbury, P. (PI)
BIOC 205: Molecular Foundations of Medicine
For medical students. The course examines the impact of molecular biology on medicine. Topics include DNA replication, recombination, and repair; genomics; gene transcription; protein translation; and proteins in cell decision-making. Medical impact is examined in patient presentations and small group discussions of papers from the medical literature.
Terms: Aut
| Units: 4
Instructors:
Chu, G. (PI)
;
Gomez-Ospina, N. (PI)
BIOC 459: Frontiers in Interdisciplinary Biosciences (BIO 459, BIOE 459, CHEM 459, CHEMENG 459, PSYCH 459)
Students register through their affiliated department; otherwise register for
CHEMENG 459. For specialists and non-specialists. Sponsored by the Stanford BioX Program. Three seminars per quarter address scientific and technical themes related to interdisciplinary approaches in bioengineering, medicine, and the chemical, physical, and biological sciences. Leading investigators from Stanford and the world present breakthroughs and endeavors that cut across core disciplines. Pre-seminars introduce basic concepts and background for non-experts. Registered students attend all pre-seminars; others welcome. See
http://biox.stanford.edu/courses/459.html. Recommended: basic mathematics, biology, chemistry, and physics.
Last offered: Spring 2020
| Repeatable
for credit
BIODS 202: BIOMEDICAL DATA SCIENCE (BIOMEDIN 202, BIOMEDIN 202P)
This course introduces the data modalities and methods valuable to ask and answer probing and novel questions that advance biomedicine. You will get exposure to a variety of current data types from imaging and omics to patient-centric and digital health generated data types. You will also be exposed to the core methodological concepts useful to analyze these data in isolation or in combination. Specifically, in four separate modules taught by expert faculty in each area the basic principles of each module will be defined and explained. Module 1, Clinical Data and Systems, will explain the basics of Electronic Health Records, and how they operate in health care settings. Next, Module 2, Image Data Health Science, will focus on an introduction to the main imaging modalities in medicine and how methodological analysis using machine vision can be used on large studies. Module 3 will focus on fusing different data streams such as clinical, imaging, molecular and other data modalities. Final
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This course introduces the data modalities and methods valuable to ask and answer probing and novel questions that advance biomedicine. You will get exposure to a variety of current data types from imaging and omics to patient-centric and digital health generated data types. You will also be exposed to the core methodological concepts useful to analyze these data in isolation or in combination. Specifically, in four separate modules taught by expert faculty in each area the basic principles of each module will be defined and explained. Module 1, Clinical Data and Systems, will explain the basics of Electronic Health Records, and how they operate in health care settings. Next, Module 2, Image Data Health Science, will focus on an introduction to the main imaging modalities in medicine and how methodological analysis using machine vision can be used on large studies. Module 3 will focus on fusing different data streams such as clinical, imaging, molecular and other data modalities. Finally, Module 4 will focus on reproducibility, evaluation and ethical issues when deploying models based on biomedical data, with emphasis on translation to practice. Emphasis will be placed questions, data and methods that advance health and medicine. Primary learning goals for this course include how to frame biomedical health questions, what data are needed to answer those questions, and what methodological constructs can be leveraged to probe and answer those questions. This course is a newly designed course for the PhD program of the Department of Biomedical Data Science but open to all. NOTE: For students in the Department of Biomedical Data Science Program, this core course MUST be taken as a letter grade only.
Terms: Win
| Units: 3
BIODS 250: Clinical Trial Design in the Age of Precision Medicine (STATS 251)
This course offers an overview of statistical foundation for modern clinical trial design in precision medicine research. Starting from a quick review of traditional clinical development paradigm through Phase I to III clinical trials for medical product approval and Phase IV post-marketing studies for safety evaluation, and challenges in the time and society costs, we will introduce recently developed innovative designs and their statistical methodology across all phases of clinical trials. You expected to learn the statistical considerations for novel phase I-II trial designs, master protocols for umbrella, platform and basket trials, adaptive and enrichment designs including subgroup selections, estimand, surrogate and composite endpoints, integration of real-world evidence and patient-focused medical product development, and meta-analysis of clinical trial endpoints. Prerequisites: Working knowledge of statistics and R.
Terms: Win
| Units: 3
BIODS 260A: Workshop in Biostatistics (STATS 260A)
Applications of data science techniques to current problems in biology, medicine and healthcare. To receive credit for one or two units, a student must attend every workshop. To receive two units, in addition to attending every workshop, the student is required to write a two page critical summary of one of the workshops, with the choice made by the student.
Terms: Aut
| Units: 1-2
| Repeatable
for credit
BIODS 260B: Workshop in Biostatistics (STATS 260B)
Applications of data science techniques to current problems in biology, medicine and healthcare. To receive credit for one or two units, a student must attend every workshop. To receive two units, in addition to attending every workshop, the student is required to write a two page critical summary of one of the workshops, with the choice made by the student
Terms: Win
| Units: 1-2
| Repeatable
for credit
BIODS 260C: Workshop in Biostatistics (STATS 260C)
Applications of data science techniques to current problems in biology, medicine and healthcare. To receive credit for one or two units, a student must attend every workshop. To receive two units, in addition to attending every workshop, the student is required to write a two page critical summary of one of the workshops, with the choice made by the student
Terms: Spr
| Units: 1-2
| Repeatable
for credit
BIODS 388: Stakeholder Competencies for Artificial Intelligence in Healthcare (BIOMEDIN 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
BIOE 10SC: Needs Finding in Healthcare
Are you on an engineering pathway and trying to decide if opportunities in healthcare might be of interest to you? Or, are you committed to a career in medicine and eager to explore how to incorporate technology innovation into your plans? In either case, Needs Finding in Healthcare is the Sophomore College for you! Several courses offered during the regular academic year provide students with the opportunity to understand healthcare problems and invent new technologies to address them. However, this is the only one that gives undergraduates the chance to directly observe the delivery of healthcare in the real world and identify important unmet needs for themselves. Needs Finding in Healthcare is a Sophomore College course offered by Stanford Biodesign. We're looking for students who are passionate about innovation and interested in how technology can be applied to help make healthcare better for patients everywhere. Over approximately three weeks, you'll spend time: Learning the funda
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Are you on an engineering pathway and trying to decide if opportunities in healthcare might be of interest to you? Or, are you committed to a career in medicine and eager to explore how to incorporate technology innovation into your plans? In either case, Needs Finding in Healthcare is the Sophomore College for you! Several courses offered during the regular academic year provide students with the opportunity to understand healthcare problems and invent new technologies to address them. However, this is the only one that gives undergraduates the chance to directly observe the delivery of healthcare in the real world and identify important unmet needs for themselves. Needs Finding in Healthcare is a Sophomore College course offered by Stanford Biodesign. We're looking for students who are passionate about innovation and interested in how technology can be applied to help make healthcare better for patients everywhere. Over approximately three weeks, you'll spend time: Learning the fundamentals of the need-driven biodesign innovation process for health technology innovation; Practicing how to conduct observations and shadow care providers to identify compelling unmet health-related needs, and then performing observations in Stanford's emergency department, operating rooms, and clinics; Conducting background research and interacting with physicians and patients to understand and prioritize needs you have been identified; Brainstorming and building early-stage prototypes to enhance your understanding of the unmet need and critical requirements for solving it; In addition, you'll meet experienced innovators from the health technology field and explore different career pathways in this dynamic space. Join us if you want to make a difference at the intersection of medicine and engineering!
Terms: Sum
| Units: 2
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