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; measurement and valuation of health; competition in health care delivery. 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: WAYSI

Grading: Medical Option (MedLtrCR/NC)
BIOMEDIN 201:
Biomedical Informatics Student Seminar
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. May be repeated three times for credit.
Terms: Aut, Win, Spr, Sum

Units: 1

Repeatable for credit

Grading: Medical Satisfactory/No Credit
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 cyberspacebig datato 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 decisionmaking based solely on published knowledge and individual physician experience. May be repeated for credit. Prerequisite: background in biomedicine. Background in computer science can be helpful but not required.
Terms: Aut

Units: 1

Grading: Medical Satisfactory/No Credit
BIOMEDIN 207:
Seminar: Health IT in Care Delivery systems
The practice of medicine is reacting quickly to the avalanche of information available from electronic health records and data directly submitted by patients and from the environment. This seminar, comprised of guest lectures from industry and academia, will highlight the practical challenges and successes of how health IT has transformed care delivery programs. The seminar will cover current efforts in clinical decision support, patientcentered design, integration with community care, big data, medical education, and the innovation pipeline for healthcare delivery organizations.
Terms: Sum

Units: 1

Grading: Medical Satisfactory/No Credit
BIOMEDIN 208:
Clinical Informatics Literature Review 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: Win

Units: 1

Grading: Medical Satisfactory/No Credit
BIOMEDIN 210:
Modeling Biomedical Systems: Ontology, Terminology, Problem Solving (CS 270)
Methods for modeling biomedical systems and for building modelbased software systems. Emphasis is on intelligent systems for decision support and Semantic Web applications. Topics: knowledge representation, controlled terminologies, ontologies, reusable problem solvers, and knowledge acquisition. Students learn about current trends in the development of advanced biomedical software systems and acquire handson experience with several systems and tools. Prerequisites: CS106A, basic familiarity with biology, probability, and logic.
Terms: Win

Units: 3

Grading: Letter or Credit/No Credit
BIOMEDIN 212:
Introduction to Biomedical Informatics Research Methodology (BIOE 212, CS 272, GENE 212)
Capstone Biomedical Informatics (BMI) experience. Handson 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.
Terms: Spr

Units: 35

Grading: Medical Option (MedLtrCR/NC)
BIOMEDIN 214:
Representations and Algorithms for Computational Molecular Biology (BIOE 214, CS 274, GENE 214)
Topics: introduction to bioinformatics and computational biology, algorithms for alignment of biological sequences and structures, computing with strings, phylogenetic tree construction, hidden Markov models, basic structural computations on proteins, protein structure prediction, protein threading techniques, homology modeling, molecular dynamics and energy minimization, statistical analysis of 3D biological data, integration of data sources, knowledge representation and controlled terminologies for molecular biology, microarray analysis, machine learning (clustering and classification), and natural language text processing. Prerequisite: CS 106B; recommended: CS161; consent of instructor for 3 units.
Terms: Aut

Units: 34

Grading: Medical Option (MedLtrCR/NC)
BIOMEDIN 215:
Data Driven Medicine
The widespread adoption of electronic health records (EHRs) has created a new source of ¿big data¿¿namely, the record of routine clinical practice¿as a byproduct of care. This graduate class will teach you how to use EHRs and other patient data to discover new clinical knowledge and improve healthcare. Upon completing this course, you should be able to: differentiate between and give examples of categories of research questions and the study designs used to address them, describe common healthcare data sources and their relative advantages and limitations, extract and transform various kinds of clinical data to create analysisready datasets, design and execute an analysis of a clinical dataset based on your familiarity with the workings, applicability, and limitations of common statistical methods, evaluate and criticize published research using your knowledge of 14 to generate new research ideas and separate hype from reality. Prerequisites: CS 106A or equivalent, STATS 60 or equivalent. Recommended: STATS 216, CS 145, STATS 305
Terms: Aut

Units: 3

Grading: Medical Option (MedLtrCR/NC)
BIOMEDIN 216:
Representations and Algorithms for Molecular Biology: Lectures
Lecture component of BIOMEDIN 214. One unit for medical and graduate students who attend lectures only; may be taken for 2 units with participation in limited assignments and final project. Lectures also available via internet. Prerequisite: familiarity with biology recommended.
Terms: Aut

Units: 12

Grading: Medical Satisfactory/No Credit
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: multiscale 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: Aut

Units: 4

Grading: Medical Option (MedLtrCR/NC)
BIOMEDIN 219:
Mathematical Models and Medical Decisions
Analytic methods for determining optimal diagnostic and therapeutic decisions with applications to the care of individual patients and the design of policies applied to patient populations. Topics include: utility theory and probability modeling, empirical methods for disease prevalence estimation, probability models for periodic processes, binary decisionmaking techniques, Markov models of dynamic disease state problems, utility assessment techniques, parametric utility models, utility models for multidimensional outcomes, analysis of timevarying clinical outcomes, and the design of costconstrained clinical policies. Extensive problem sets compliment the lectures. Prerequisites: introduction to calculus and basic statistics.
Terms: Win

Units: 3

Grading: Medical Option (MedLtrCR/NC)
BIOMEDIN 221:
Machine Learning Approaches for Data Fusion in Biomedicine
Vast amounts of biomedical data are now routinely available for patients, raging from genomic data, to radiographic images and electronic health records. AI and machine learning are increasingly used to enable pattern discover to link such data for improvements in patient diagnosis, prognosis and tailoring treatment response. Yet, few studies focus on how to link different types of biomedical data in synergistic ways, and to develop data fusion approaches for improved biomedical decision support. This course will describe approaches for multiomics, multimodal and multiscale data fusion of biomedical data in the context of biomedical decision support. Prerequisites: CS106A or equivalent, Stats 60 or equivalent.
Terms: Aut

Units: 2

Grading: Medical Option (MedLtrCR/NC)
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, Win, Spr, Sum

Units: 3

Grading: Medical Option (MedLtrCR/NC)
BIOMEDIN 225:
Data Driven Medicine: Lectures
Lectures for BIOMEDIN 215.With the spread of electronic health records and increasingly low cost assays for patient molecular data, powerful data repositories with tremendous potential for biomedical research, clinical care and personalized medicine are being built. But these databases are large and difficult for any one specialist to analyze. To find the hidden associations within the full set of data, we introduce methods for datamining at the internet scale, the handling of largescale electronic medical records data for machine learning, methods in natural language processing and textmining applied to medical records, methods for using ontologies for the annotation and indexing of unstructured content as well as semantic web technologies. Prerequisites: familiarity with statistics (STATS 216) and biology.
Terms: Aut

Units: 2

Grading: Medical Option (MedLtrCR/NC)
BIOMEDIN 226:
Digital Health Practicum in a Health Care Delivery System
Practical experience implementing clinical informatics solutions with a focus on digital health in one of the largest healthcare delivery systems in the United States. Individual meetings with senior clinical informatics leaders to discuss elements of successful projects. Implementation opportunities include supporting the use of electronic health records, engagement of patients and providers via a personal health record, use of informatics to support patient service centers, and improvement of patient access to clinical data. Consent of course instructors required at least one quarter prior to student enrollment in course.
Terms: Aut, Win, Spr, Sum

Units: 23

Grading: Medical Option (MedLtrCR/NC)
BIOMEDIN 233:
Intermediate Biostatistics: Analysis of Discrete Data (HRP 261, STATS 261)
Methods for analyzing data from casecontrol and crosssectional studies: the 2x2 table, chisquare test, Fisher's exact test, odds ratios, MantelHaenzel methods, stratification, tests for matched data, logistic regression, conditional logistic regression. Emphasis is on data analysis in SAS. Special topics: crossfold validation and bootstrap inference.
Terms: Win

Units: 3

Grading: Medical Option (MedLtrCR/NC)
BIOMEDIN 245:
Statistical and Machine Learning Methods for Genomics (BIO 268, CS 373, GENE 245, STATS 345)
Introduction to statistical and computational methods for genomics. Sample topics include: expectation maximization, hidden Markov model, Markov chain Monte Carlo, ensemble learning, probabilistic graphical models, kernel methods and other modern machine learning paradigms. Rationales and techniques illustrated with existing implementations used in population genetics, disease association, and functional regulatory genomics studies. Instruction includes lectures and discussion of readings from primary literature. Homework and projects require implementing some of the algorithms and using existing toolkits for analysis of genomic datasets.
Terms: Win

Units: 3

Grading: Medical Option (MedLtrCR/NC)
BIOMEDIN 251:
Outcomes Analysis (HRP 252, MED 252)
Methods of conducting empirical studies which use large existing medical, survey, and other databases to ask both clinical and policy questions. Econometric and statistical models used to conduct medical outcomes research. How research is conducted on medical and health economics questions when a randomized trial is impossible. Problem sets emphasize handson data analysis and application of methods, including reanalyses of wellknown studies. Prerequisites: one or more courses in probability, and statistics or biostatistics.
Terms: Spr

Units: 4

Grading: Medical Option (MedLtrCR/NC)
BIOMEDIN 256:
Economics of Health and Medical Care (BIOMEDIN 156, 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; measurement and valuation of health; competition in health care delivery. 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

Grading: Medical Option (MedLtrCR/NC)
BIOMEDIN 260:
Computational Methods for Biomedical Image Analysis and Interpretation (CS 235, RAD 260)
The latest biological and medical imaging modalities and their applications in research and medicine. Focus is on computational analytic and interpretive approaches to optimize extraction and use of biological and clinical imaging data for diagnostic and therapeutic translational medical applications. Topics include major image databases, fundamental methods in image processing and quantitative extraction of image features, structured recording of image information including semantic features and ontologies, indexing, search and contentbased image retrieval. Case studies include linking image data to genomic, phenotypic and clinical data, developing representations of image phenotypes for use in medical decision support and research applications and the role that biomedical imaging informatics plays in new questions in biomedical science. Includes a project. Enrollment for 3 units requires instructor consent. Prerequisites: programming ability at the level of CS 106A, familiarity with statistics, basic biology. Knowledge of Matlab or Python highly recommended.
Terms: Spr

Units: 34

Grading: Medical Option (MedLtrCR/NC)
BIOMEDIN 273A:
The Human Genome Source Code (CS 273A, DBIO 273A)
A computational introduction to the most amazing programming language on the planet: your genome. Topics include genome sequencing (assembling source code from code fragments); the human genome functional landscape: variable assignments (genes), controlflow logic (gene regulation) and runtime stack (epigenomics); human disease and personalized genomics (as a hunt for bugs in the human code); genome editing (code injection) to cure the incurable; and the source code behind amazing animal adaptations. Algorithmic approaches will introduce ideas from computational genomics, machine learning and natural language processing. Course includes primers on molecular biology, and text processing languages. Prerequisites: CS106B or equivalent.
Terms: Win

Units: 3

Grading: Letter or Credit/No Credit
BIOMEDIN 273B:
Deep Learning in Genomics and Biomedicine (BIODS 237, CS 273B, GENE 236)
Recent breakthroughs in highthroughput genomic and biomedical data are transforming biological sciences into "big data" disciplines. In parallel, progress in deep neural networks are revolutionizing fields such as image recognition, natural language processing and, more broadly, AI. This course explores the exciting intersection between these two advances. The course will start with an introduction to deep learning and overview the relevant background in genomics and highthroughput biotechnology, focusing on the available data and their relevance. It will then cover the ongoing developments in deep learning (supervised, unsupervised and generative models) with the focus on the applications of these methods to biomedical data, which are beginning to produced dramatic results. In addition to predictive modeling, the course emphasizes how to visualize and extract interpretable, biological insights from such models. Recent papers from the literature will be presented and discussed. Students will be introduced to and work with popular deep learning software frameworks. Students will work in groups on a final class project using real world datasets. Prerequisites: College calculus, linear algebra, basic probability and statistics such as CS 109, and basic machine learning such as CS 229. No prior knowledge of genomics is necessary.
Terms: Aut

Units: 3

Grading: Medical Option (MedLtrCR/NC)
BIOMEDIN 279:
Computational Biology: Structure and Organization of Biomolecules and Cells (BIOE 279, BIOPHYS 279, CME 279, CS 279)
Computational techniques for investigating and designing the threedimensional 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, cellularlevel 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

Grading: Letter or Credit/No Credit
BIOMEDIN 290:
Biomedical Informatics Teaching Methods
Handson training in biomedical informatics pedagogy. Practical experience in pedagogical approaches, variously including didactic, inquiry, project, team, case, field, and/or problembased approaches. Students create course content, including lectures, exercises, and assessments, and evaluate learning activities and outcomes. Prerequisite: instructor consent.
Terms: Aut, Win, Spr, Sum

Units: 16

Repeatable for credit

Grading: Medical Option (MedLtrCR/NC)
Instructors: ;
Altman, R. (PI);
Ashley, E. (PI);
Bassik, M. (PI);
Batzoglou, S. (PI);
Bayati, M. (PI);
Bejerano, G. (PI);
Bhattacharya, J. (PI);
Blish, C. (PI);
Boahen, K. (PI);
Brandeau, M. (PI);
Bustamante, C. (PI);
Butte, A. (PI);
Chang, H. (PI);
Cherry, J. (PI);
Cohen, S. (PI);
Covert, M. (PI);
Curtis, C. (PI);
Das, A. (PI);
Das, R. (PI);
Davis, R. (PI);
Delp, S. (PI);
Desai, M. (PI);
Dill, D. (PI);
Dumontier, M. (PI);
Elias, J. (PI);
Fagan, L. (PI);
Feldman, M. (PI);
Ferrell, J. (PI);
Fraser, H. (PI);
Gambhir, S. (PI);
Gerritsen, M. (PI);
Gevaert, O. (PI);
Goldstein, M. (PI);
Greenleaf, W. (PI);
Guibas, L. (PI);
Hastie, T. (PI);
Hlatky, M. (PI);
Holmes, S. (PI);
Ji, H. (PI);
Karp, P. (PI);
Khatri, P. (PI);
Kim, S. (PI);
Kirkegaard, K. (PI);
Klein, T. (PI);
Koller, D. (PI);
Krummel, T. (PI);
Kundaje, A. (PI);
Levitt, M. (PI);
Levitt, R. (PI);
Li, J. (PI);
Longhurst, C. (PI);
Lowe, H. (PI);
Mallick, P. (PI);
Manning, C. (PI);
McAdams, H. (PI);
Menon, V. (PI);
Montgomery, S. (PI);
Musen, M. (PI);
Napel, S. (PI);
Nolan, G. (PI);
Olshen, R. (PI);
Owen, A. (PI);
Owens, D. (PI);
Paik, D. (PI);
Palacios, J. (PI);
Pande, V. (PI);
Petrov, D. (PI);
Plevritis, S. (PI);
Poldrack, R. (PI);
Pritchard, J. (PI);
Relman, D. (PI);
RiedelKruse, I. (PI);
Rivas, M. (PI);
Rubin, D. (PI);
Sabatti, C. (PI);
Salzman, J. (PI);
Shachter, R. (PI);
Shafer, R. (PI);
Shah, N. (PI);
Sherlock, G. (PI);
Sidow, A. (PI);
Snyder, M. (PI);
Tang, H. (PI);
Taylor, C. (PI);
Theriot, J. (PI);
Tibshirani, R. (PI);
Utz, P. (PI);
Walker, M. (PI);
Wall, D. (PI);
Winograd, T. (PI);
Wong, W. (PI);
Xing, L. (PI);
Zou, J. (PI)
BIOMEDIN 299:
Directed Reading and Research
For students wishing to receive credit for directed reading or research time. Prerequisite: consent of instructor. (Staff)
Terms: Aut, Win, Spr, Sum

Units: 118

Repeatable for credit

Grading: Medical Option (MedLtrCR/NC)
Instructors: ;
Altman, R. (PI);
Ashley, E. (PI);
Baiocchi, M. (PI);
Bassik, M. (PI);
Batzoglou, S. (PI);
Bayati, M. (PI);
Bejerano, G. (PI);
Bhattacharya, J. (PI);
Blish, C. (PI);
Boahen, K. (PI);
Brandeau, M. (PI);
Brutlag, D. (PI);
Bustamante, C. (PI);
Butte, A. (PI);
Chang, H. (PI);
Chen, J. (PI);
Cherry, J. (PI);
Cohen, S. (PI);
Covert, M. (PI);
Curtis, C. (PI);
Das, A. (PI);
Das, R. (PI);
Davis, R. (PI);
Delp, S. (PI);
Desai, M. (PI);
Dill, D. (PI);
Dror, R. (PI);
Dumontier, M. (PI);
Elias, J. (PI);
Fagan, L. (PI);
Feldman, M. (PI);
Ferrell, J. (PI);
Fraser, H. (PI);
Gambhir, S. (PI);
Gentles, A. (PI);
Gerritsen, M. (PI);
Gevaert, O. (PI);
Goldstein, M. (PI);
Greenleaf, W. (PI);
Guibas, L. (PI);
Hastie, T. (PI);
HernandezBoussard, T. (PI);
Hlatky, M. (PI);
Holmes, S. (PI);
Ji, H. (PI);
Karp, P. (PI);
Khatri, P. (PI);
Kim, S. (PI);
Kirkegaard, K. (PI);
Klein, T. (PI);
Koller, D. (PI);
Krummel, T. (PI);
Kundaje, A. (PI);
Langlotz, C. (PI);
Levitt, M. (PI);
Li, J. (PI);
Longhurst, C. (PI);
Lowe, H. (PI);
Mallick, P. (PI);
Manning, C. (PI);
McAdams, H. (PI);
Menon, V. (PI);
Montgomery, S. (PI);
Musen, M. (PI);
Napel, S. (PI);
Nolan, G. (PI);
Olshen, R. (PI);
Owen, A. (PI);
Owens, D. (PI);
Paik, D. (PI);
Palacios, J. (PI);
Pande, V. (PI);
Petrov, D. (PI);
Plevritis, S. (PI);
Poldrack, R. (PI);
Pritchard, J. (PI);
Relman, D. (PI);
RiedelKruse, I. (PI);
Rivas, M. (PI);
Rubin, D. (PI);
Sabatti, C. (PI);
Salzman, J. (PI);
Shachter, R. (PI);
Shafer, R. (PI);
Shah, N. (PI);
Sherlock, G. (PI);
Sidow, A. (PI);
Snyder, M. (PI);
Tang, H. (PI);
Taylor, C. (PI);
Theriot, J. (PI);
Tibshirani, R. (PI);
Tu, S. (PI);
Utz, P. (PI);
Walker, M. (PI);
Wall, D. (PI);
Winograd, T. (PI);
Wong, W. (PI);
Xing, L. (PI);
Zou, J. (PI)
BIOMEDIN 304:
Clinical Experience Seminar for Students in Biomedical Informatics
This seminar is intended to expose Biomedical Informatics graduate students to clinical environments where informatics is being applied. Students will shadow clinical care and interact with physicians and other allied health professionals throughout Stanford Healthcare and Stanford Children's Health during weekly sessions. Students will be asked to reflect on their experiences and discuss future applications to informatics projects. Preference will be given to senior students. Requires Course Director approval for enrollment  students should register 30 days prior to the first day of class for consideration. Prerequisites: School of Medicine HIPAA Training; Occupational Health clearance; SHC Compliance Attestation. All prerequisites must be submitted 2 weeks before the 1st day in order to ensure hospital compliance.
Terms: Win

Units: 1

Grading: Medical Satisfactory/No Credit
BIOMEDIN 370:
Medical Scholars Research
Provides an opportunity for student and faculty interaction, as well as academic credit and financial support, to medical students who undertake original research. Enrollment is limited to students with approved projects.
Terms: Aut, Win, Spr, Sum

Units: 418

Repeatable for credit

Grading: Medical School MD Grades
Instructors: ;
Altman, R. (PI);
Ashley, E. (PI);
Bassik, M. (PI);
Batzoglou, S. (PI);
Bayati, M. (PI);
Bejerano, G. (PI);
Bhattacharya, J. (PI);
Blish, C. (PI);
Boahen, K. (PI);
Brandeau, M. (PI);
Brutlag, D. (PI);
Bustamante, C. (PI);
Butte, A. (PI);
Chang, H. (PI);
Chen, J. (PI);
Cherry, J. (PI);
Cohen, S. (PI);
Covert, M. (PI);
Curtis, C. (PI);
Das, A. (PI);
Das, R. (PI);
Davis, R. (PI);
Delp, S. (PI);
Desai, M. (PI);
Dill, D. (PI);
Dumontier, M. (PI);
Elias, J. (PI);
Fagan, L. (PI);
Feldman, M. (PI);
Ferrell, J. (PI);
Fraser, H. (PI);
Gambhir, S. (PI);
Gerritsen, M. (PI);
Gevaert, O. (PI);
Goldstein, M. (PI);
Greenleaf, W. (PI);
Guibas, L. (PI);
Hastie, T. (PI);
Hlatky, M. (PI);
Holmes, S. (PI);
Ji, H. (PI);
Karp, P. (PI);
Khatri, P. (PI);
Kim, S. (PI);
Kirkegaard, K. (PI);
Klein, T. (PI);
Koller, D. (PI);
Krummel, T. (PI);
Kundaje, A. (PI);
Levitt, M. (PI);
Li, J. (PI);
Longhurst, C. (PI);
Lowe, H. (PI);
Mallick, P. (PI);
Manning, C. (PI);
McAdams, H. (PI);
Menon, V. (PI);
Montgomery, S. (PI);
Musen, M. (PI);
Napel, S. (PI);
Nolan, G. (PI);
Olshen, R. (PI);
Owen, A. (PI);
Owens, D. (PI);
Paik, D. (PI);
Palacios, J. (PI);
Pande, V. (PI);
Petrov, D. (PI);
Plevritis, S. (PI);
Poldrack, R. (PI);
Pritchard, J. (PI);
Relman, D. (PI);
RiedelKruse, I. (PI);
Rivas, M. (PI);
Rubin, D. (PI);
Sabatti, C. (PI);
Salzman, J. (PI);
Shachter, R. (PI);
Shafer, R. (PI);
Shah, N. (PI);
Sherlock, G. (PI);
Sidow, A. (PI);
Snyder, M. (PI);
Tang, H. (PI);
Taylor, C. (PI);
Theriot, J. (PI);
Tibshirani, R. (PI);
Tu, S. (PI);
Utz, P. (PI);
Walker, M. (PI);
Wall, D. (PI);
Winograd, T. (PI);
Wong, W. (PI);
Xing, L. (PI);
Zou, J. (PI)
BIOMEDIN 390A:
Curricular Practical Training
Provides educational opportunities in biomedical informatics research. Qualified biomedical informatics students engage in internship work and integrate that work into their academic program. Students register during the quarter they are employed and must complete a research report outlining their work activity, problems investigated, key results, and any followup on projects they expect to perform. BIOMEDIN 390A, B, and C may each be taken only once.
Terms: Aut, Win, Spr, Sum

Units: 1

Grading: Medical Satisfactory/No Credit
BIOMEDIN 390B:
Curricular Practical Training
Provides educational opportunities in biomedical informatics research. Qualified biomedical informatics students engage in internship work and integrate that work into their academic program. Students register during the quarter they are employed and must complete a research report outlining their work activity, problems investigated, key results, and any followup on projects they expect to perform. BIOMEDIN 390A, B, and C may each be taken only once.
Terms: Aut, Win, Spr, Sum

Units: 1

Grading: Medical Satisfactory/No Credit
BIOMEDIN 390C:
Curricular Practical Training
Provides educational opportunities in biomedical informatics research. Qualified biomedical informatics students engage in internship work and integrate that work into their academic program. Students register during the quarter they are employed and must complete a research report outlining their work activity, problems investigated, key results, and any followup on projects they expect to perform. BIOMEDIN 390A, B, and C may each be taken only once.
Terms: Aut, Win, Spr, Sum

Units: 1

Grading: Medical Satisfactory/No Credit
BIOMEDIN 432:
Analysis of Costs, Risks, and Benefits of Health Care (HRP 392)
For graduate students. How to do cost/benefit analysis when the output is difficult or impossible to measure. Literature on the principles of cost/benefit analysis applied to health care. Critical review of actual studies. Emphasis is on the art of practical application.
Terms: Aut

Units: 4

Grading: Medical Option (MedLtrCR/NC)
BIOMEDIN 801:
TGR Master's Project
Project credit for masters students who have completed all course requirements and minimum of 45 Stanford units.
Terms: Aut, Win, Spr, Sum

Units: 0

Repeatable for credit

Grading: TGR
Instructors: ;
Altman, R. (PI);
Ashley, E. (PI);
Bassik, M. (PI);
Batzoglou, S. (PI);
Bayati, M. (PI);
Bejerano, G. (PI);
Bhattacharya, J. (PI);
Blish, C. (PI);
Boahen, K. (PI);
Brandeau, M. (PI);
Brutlag, D. (PI);
Bustamante, C. (PI);
Butte, A. (PI);
Chang, H. (PI);
Cherry, J. (PI);
Cohen, S. (PI);
Covert, M. (PI);
Curtis, C. (PI);
Das, A. (PI);
Das, R. (PI);
Davis, R. (PI);
Delp, S. (PI);
Desai, M. (PI);
Dill, D. (PI);
Dumontier, M. (PI);
Elias, J. (PI);
Fagan, L. (PI);
Feldman, M. (PI);
Ferrell, J. (PI);
Fraser, H. (PI);
Gambhir, S. (PI);
Gentles, A. (PI);
Gerritsen, M. (PI);
Gevaert, O. (PI);
Goldstein, M. (PI);
Greenleaf, W. (PI);
Guibas, L. (PI);
Hastie, T. (PI);
Hlatky, M. (PI);
Holmes, S. (PI);
Ji, H. (PI);
Karp, P. (PI);
Khatri, P. (PI);
Kim, S. (PI);
Kirkegaard, K. (PI);
Klein, T. (PI);
Koller, D. (PI);
Krummel, T. (PI);
Kundaje, A. (PI);
Levitt, M. (PI);
Li, J. (PI);
Longhurst, C. (PI);
Lowe, H. (PI);
Mallick, P. (PI);
Manning, C. (PI);
McAdams, H. (PI);
Menon, V. (PI);
Montgomery, S. (PI);
Musen, M. (PI);
Napel, S. (PI);
Nolan, G. (PI);
Olshen, R. (PI);
Owen, A. (PI);
Owens, D. (PI);
Paik, D. (PI);
Palacios, J. (PI);
Pande, V. (PI);
Petrov, D. (PI);
Plevritis, S. (PI);
Poldrack, R. (PI);
Pritchard, J. (PI);
Relman, D. (PI);
RiedelKruse, I. (PI);
Rivas, M. (PI);
Rubin, D. (PI);
Sabatti, C. (PI);
Salzman, J. (PI);
Shachter, R. (PI);
Shafer, R. (PI);
Shah, N. (PI);
Sherlock, G. (PI);
Sidow, A. (PI);
Snyder, M. (PI);
Tang, H. (PI);
Taylor, C. (PI);
Theriot, J. (PI);
Tibshirani, R. (PI);
Tu, S. (PI);
Utz, P. (PI);
Walker, M. (PI);
Wall, D. (PI);
Winograd, T. (PI);
Wong, W. (PI);
Xing, L. (PI);
Zou, J. (PI)
BIOMEDIN 802:
TGR PhD Dissertation
Terms: Aut, Win, Spr, Sum

Units: 0

Repeatable for credit

Grading: TGR
Instructors: ;
Altman, R. (PI);
Ashley, E. (PI);
Bassik, M. (PI);
Batzoglou, S. (PI);
Bayati, M. (PI);
Bejerano, G. (PI);
Bhattacharya, J. (PI);
Blish, C. (PI);
Boahen, K. (PI);
Brandeau, M. (PI);
Brutlag, D. (PI);
Bustamante, C. (PI);
Butte, A. (PI);
Chang, H. (PI);
Cherry, J. (PI);
Cohen, S. (PI);
Covert, M. (PI);
Curtis, C. (PI);
Das, A. (PI);
Das, R. (PI);
Davis, R. (PI);
Delp, S. (PI);
Desai, M. (PI);
Dill, D. (PI);
Dumontier, M. (PI);
Elias, J. (PI);
Fagan, L. (PI);
Feldman, M. (PI);
Ferrell, J. (PI);
Fraser, H. (PI);
Gambhir, S. (PI);
Gerritsen, M. (PI);
Gevaert, O. (PI);
Goldstein, M. (PI);
Greenleaf, W. (PI);
Guibas, L. (PI);
Hastie, T. (PI);
Hlatky, M. (PI);
Holmes, S. (PI);
Ji, H. (PI);
Karp, P. (PI);
Khatri, P. (PI);
Kim, S. (PI);
Kirkegaard, K. (PI);
Klein, T. (PI);
Koller, D. (PI);
Krummel, T. (PI);
Kundaje, A. (PI);
Levitt, M. (PI);
Li, J. (PI);
Longhurst, C. (PI);
Lowe, H. (PI);
Mallick, P. (PI);
Manning, C. (PI);
McAdams, H. (PI);
Menon, V. (PI);
Montgomery, S. (PI);
Musen, M. (PI);
Napel, S. (PI);
Nolan, G. (PI);
Olshen, R. (PI);
Owen, A. (PI);
Owens, D. (PI);
Paik, D. (PI);
Palacios, J. (PI);
Pande, V. (PI);
Petrov, D. (PI);
Plevritis, S. (PI);
Poldrack, R. (PI);
Pritchard, J. (PI);
Relman, D. (PI);
RiedelKruse, I. (PI);
Rivas, M. (PI);
Rubin, D. (PI);
Sabatti, C. (PI);
Salzman, J. (PI);
Shachter, R. (PI);
Shafer, R. (PI);
Shah, N. (PI);
Sherlock, G. (PI);
Sidow, A. (PI);
Snyder, M. (PI);
Tang, H. (PI);
Taylor, C. (PI);
Theriot, J. (PI);
Tibshirani, R. (PI);
Tu, S. (PI);
Utz, P. (PI);
Walker, M. (PI);
Wall, D. (PI);
Winograd, T. (PI);
Wong, W. (PI);
Xing, L. (PI);
Zou, J. (PI)