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1 - 10 of 23 results for: BIOMEDIN ; Currently searching spring courses. You can expand your search to include all quarters

BIOMEDIN 156: Economics of Health and Medical Care (BIOMEDIN 256, ECON 126, HRP 256)

Institutional, theoretical, and empirical analysis of the problems of health and medical care. Topics: demand for medical care and medical insurance; institutions in the health sector; economics of information applied to the market for health insurance and for health care; economics of health care labor markets and health care production; and economic epidemiology. Graduate students with research interests should take ECON 249. Prerequisites: ECON 50 and either ECON 102A or STATS 116 or the equivalent. Recommended: ECON 51.
Terms: Spr | Units: 5 | UG Reqs: WAY-SI

BIOMEDIN 201: Biomedical Informatics Student Seminar (BIODS 201)

Participants report on recent articles from the Biomedical Informatics literature or their research projects. Goals are to teach critical reading of scientific papers and presentation skills. Summer Quarter consists of critical review of relevant literature led by faculty associated with the Biomedical Informatics Program. May be repeated three times for credit.
Terms: Aut, Win, Spr, Sum | Units: 1 | Repeatable 3 times (up to 3 units total)

BIOMEDIN 206: Informatics in Industry

Effective management, modeling, acquisition, and mining of biomedical information in healthcare and biotechnology companies and approaches to information management adopted by companies in this ecosystem. Guest speakers from pharmaceutical/biotechnology companies, clinics/hospitals, health communities/portals, instrumentation/software vendors. May be repeated for credit.
Terms: Spr | Units: 1 | Repeatable 12 times (up to 12 units total)

BIOMEDIN 208: Applied Clinical Informatics Seminar

Weekly seminar series in which seminal literature and current publications in the field of clinical informatics are reviewed and discussed. Organized by the Stanford Clinical Informatics fellowship program. Topics include electronic health record design, implementation, and evaluation; patient engagement; provider satisfaction; and hot topics in clinical informatics. Limited enrollment.
Terms: Spr | Units: 1 | Repeatable 2 times (up to 2 units total)

BIOMEDIN 210: Modeling Biomedical Systems (CS 270)

At the core of informatics is the problem of creating computable models of biomedical phenomena. This course explores methods for modeling biomedical systems with an emphasis on contemporary semantic technology, including knowledge graphs. Topics: data modeling, knowledge representation, controlled terminologies, ontologies, reusable problem solvers, modeling problems in healthcare information technology and other aspects of informatics. Students acquire hands-on experience with several systems and tools. Prerequisites: CS106A. Basic familiarity with Python programming, biology, probability, and logic are assumed.
Terms: Win, Spr | Units: 3
Instructors: Musen, M. (PI)

BIOMEDIN 212: Introduction to Biomedical Data Science Research Methodology (BIOE 212, CS 272, GENE 212)

Capstone Biomedical Data Science experience. Hands-on software building. Student teams conceive, design, specify, implement, evaluate, and report on a software project in the domain of biomedicine. Creating written proposals, peer review, providing status reports, and preparing final reports. Issues related to research reproducibility. Guest lectures from professional biomedical informatics systems builders on issues related to the process of project management. Software engineering basics. Because the team projects start in the first week of class, attendance that week is strongly recommended. Prerequisites: BIOMEDIN 210 or 214 or 215 or 217 or 260. Preference to BMI graduate students. Consent of instructor required.NOTE: For students in the Department of Biomedical Data Science Program, this core course MUST be taken as a letter grade only.
Terms: Spr | Units: 3-5

BIOMEDIN 217: Translational Bioinformatics (BIOE 217, CS 275, GENE 217)

Computational methods for the translation of biomedical data into diagnostic, prognostic, and therapeutic applications in medicine. Topics: multi-scale omics data generation and analysis, utility and limitations of public biomedical resources, machine learning and data mining, issues and opportunities in drug discovery, and mobile/digital health solutions. Case studies and course project. Prerequisites: programming ability at the level of CS 106A and familiarity with biology and statistics.
Terms: Win, Spr | Units: 3-4

BIOMEDIN 222: Cloud Computing for Biology and Healthcare (CS 273C, GENE 222)

Big Data is radically transforming healthcare. To provide real-time personalized healthcare, we need hardware and software solutions that can efficiently store and process large-scale biomedical datasets. In this class, students will learn the concepts of cloud computing and parallel systems' architecture. This class prepares students to understand how to design parallel programs for computationally intensive medical applications and how to run these applications on computing frameworks such as Cloud Computing and High Performance Computing (HPC) systems. Prerequisites: familiarity with programming in Python and R.
Terms: Spr | Units: 3

BIOMEDIN 223: Deploying and Evaluating Fair AI in Healthcare (EPI 220)

AI applications are proliferating throughout the healthcare system and stakeholders are faced with the opportunities and challenges of deploying these quickly evolving technologies. This course teaches the principles of AI evaluations in healthcare, provides a framework for deployment of AI in the healthcare system, reviews the regulatory environment, and discusses fundamental components used to evaluate the downstream effects of AI healthcare solutions, including biases and fairness. Prerequisites: CS106A; familiarity with statistics ( stats 202), BIOMED 215, or BIODS 220
Terms: Spr | Units: 2-3

BIOMEDIN 224: Principles of Pharmacogenomics (GENE 224)

This course is an introduction to pharmacogenomics, including the relevant pharmacology, genomics, experimental methods (sequencing, expression, genotyping), data analysis methods and bioinformatics. The course reviews key gene classes (e.g., cytochromes, transporters) and key drugs (e.g., warfarin, clopidogrel, statins, cancer drugs) in the field. Resources for pharmacogenomics (e.g., PharmGKB, Drugbank, NCBI resources) are reviewed, as well as issues implementing pharmacogenomics testing in the clinical setting. Reading of key papers, including student presentations of this work; problem sets; final project selected with approval of instructor. Prerequisites: two of BIO 41, 42, 43, 44X, 44Y or consent of instructor.
Terms: Aut, Spr | Units: 3
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