printer friendly pageBIOMEDIN 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 248. Prerequisites:
ECON 50 and
ECON 102A or
Stats 116 or the equivalent. Recommended:
ECON 51.
Terms: Aut
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Units: 5
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Grading: Letter or Credit/No Credit
Instructors:
Dickstein, M. (PI)
;
Foo, P. (PI)
;
Foo, P. (TA)
;
Wong, P. (TA)
...
more instructors for BIOMEDIN 156 »
Instructors:
Dickstein, M. (PI)
;
Foo, P. (PI)
;
Foo, P. (TA)
;
Wong, P. (TA)
BIOMEDIN 200: Biomedical Informatics Colloquium
Series of colloquia offered by program faculty, students, and occasional guest lecturers. May be repeated three times for credit.
Terms: Aut, Win, Spr
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Units: 1
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Repeatable for credit
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Grading: Satisfactory/No Credit
Instructors:
Musen, M. (PI)
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
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Units: 1
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Repeatable for credit
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Grading: Satisfactory/No Credit
Instructors:
Musen, M. (PI)
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. May be repeated for credit. Prerequisite: background in biomedicine. Background in computer science can be helpful but not required.
Terms: Aut
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Units: 1
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Repeatable for credit
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Grading: Medical Satisfactory/No Credit
Instructors:
Rubin, D. (PI)
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, Gibbs Sampling, 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. Prerequisites: programming skills; consent of instructor for 3 units.
Terms: Aut
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Units: 3-4
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Grading: Medical Option (Med-Ltr-CR/NC)
Instructors:
Altman, R. (PI)
BIOMEDIN 215: Data Driven Medicine
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 data-mining at the internet scale, the handling of large-scale electronic medical records data for machine learning, methods in natural language processing and text-mining applied to medical records, methods for using ontologies for the annotation and indexing of unstructured content as well as semantic web technologies. Prerequisites:
CS 106A; familiarity with statistics (
STATS 202) and biology. Recommended: one of
CS 246 (previously
CS 345A),
STATS 305,
CS 229.
Terms: Aut
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Units: 3
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Grading: Medical Option (Med-Ltr-CR/NC)
Instructors:
Shah, N. (PI)
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
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Units: 1-2
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Grading: Medical Satisfactory/No Credit
Instructors:
Altman, R. (PI)
BIOMEDIN 225: Data Driven Medicine: Lectures
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 data-mining at the internet scale, the handling of large-scale electronic medical records data for machine learning, methods in natural language processing and text-mining applied to medical records, methods for using ontologies for the annotation and indexing of unstructured content as well as semantic web technologies. Prerequisites:
Biomedin 210 highly recommended;
CS 106A,
CS 345A recommended.
Terms: Aut
|
Units: 2
|
Grading: Medical Option (Med-Ltr-CR/NC)
Instructors:
Shah, N. (PI)
BIOMEDIN 231: Computational Molecular Biology (BIOC 218)
Practical, hands-on approach to field of computational molecular biology. Recommended for molecular biologists and computer scientists desiring to understand the major issues concerning analysis of genomes, sequences and structures. Various existing methods critically described and strengths and limitations of each. Practical assignments utilizing tools described. Prerequisite:
BIO 41 or consent of instructor. All homework and coursework submitted electronically. Course webpage:
https://biochem218.stanford.edu/.
Terms: Aut, Win, Spr
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Units: 3
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Grading: Medical Option (Med-Ltr-CR/NC)
Instructors:
Brutlag, D. (PI)
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 248. Prerequisites:
ECON 50 and
ECON 102A or
Stats 116 or the equivalent. Recommended:
ECON 51.
Terms: Aut
|
Units: 5
|
Grading: Letter or Credit/No Credit
Instructors:
Dickstein, M. (PI)
;
Foo, P. (PI)
;
Foo, P. (TA)
;
Wong, P. (TA)
...
more instructors for BIOMEDIN 256 »
Instructors:
Dickstein, M. (PI)
;
Foo, P. (PI)
;
Foo, P. (TA)
;
Wong, P. (TA)
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