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1 - 10 of 41 results for: BIOMEDIN

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 173A: Foundations of Computational Human Genomics (CS 173A, DBIO 173A)

(Only one of 173A or 273A counts toward any CS degree program.) A coder's primer to Computational Biology through the most amazing "source code" known: your genome. Examine the major forces of genome "code development" - positive, negative and neutral selection. Learn about genome sequencing (discovering your source code from fragments); genome content: variables (genes), control-flow (gene regulation), run-time stacks (epigenomics) and memory leaks (repeats); personalized genomics and genetic disease (code bugs); genome editing (code injection); ultra conservation (unsolved mysteries) and code modifications behind amazing animal adaptations. Course includes primers on molecular biology and text processing. Prerequisites: comfortable coding in Python from the command line.
Terms: Win | Units: 3-4

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 202: BIOMEDICAL DATA SCIENCE (BIODS 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 more »
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

BIOMEDIN 202P: BIOMEDICAL DATA SCIENCE (BIODS 202, BIOMEDIN 202)

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 more »
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.

BIOMEDIN 205: Precision Practice with Big Data

Primarily for M.D. students; open to other graduate students. Provides an overview of how to leverage large amounts of clinical, molecular, and imaging data within hospitals and in cyberspace--big data--to practice medicine more effectively. Lectures by physicians, researchers, and industry leaders survey how the major methods of informatics can help physicians leverage big data to profile disease, to personalize treatment to patients, to predict treatment response, to discover new knowledge, and to challenge established medical dogma and the current paradigm of clinical decision-making based solely on published knowledge and individual physician experience. Prerequisite: background in biomedicine. Background in computer science can be helpful but not required.
Terms: Aut | Units: 1

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
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