printer friendly pageBIOMEDIN 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 by-product 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 analysis-ready 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 1-4 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
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
| Units: 4
BIOMEDIN 218: Translational Bioinformatics Lectures
Same content as
BIOMEDIN 217; for medical and graduate students who attend lectures and participate in limited assignments and final project. 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.nPrerequisites: programming ability at the level of
CS 106A and familiarity with biology and statistics.
Terms: Win
| Units: 2
Instructors:
Gevaert, O. (PI)
;
Wall, D. (PI)
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 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: familiarity with statistics (
STATS 216) and biology.
Terms: Aut
| Units: 2
BIOMEDIN 260: Computational Methods for Biomedical Image Analysis and Interpretation (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 content-based 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: 3-4
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 three-dimensional 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, cellular-level 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
Instructors:
Dror, R. (PI)
;
Bedi, R. (TA)
;
El-Gabalawy, O. (TA)
...
more instructors for BIOMEDIN 279 »
Instructors:
Dror, R. (PI)
;
Bedi, R. (TA)
;
El-Gabalawy, O. (TA)
;
Fernandes, D. (TA)
;
Paggi, J. (TA)
;
Sanborn, A. (TA)
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: Aut, Win
| Units: 1
Instructors:
Stevens, L. (PI)
BIOMEDIN 371: Computational Biology in Four Dimensions (BIOPHYS 371, CME 371, CS 371)
Cutting-edge research on computational techniques for investigating and designing the three-dimensional structure and dynamics of biomolecules, cells, and everything in between. These techniques, which draw on approaches ranging from physics-based simulation to machine learning, play an increasingly important role in drug discovery, medicine, bioengineering, and molecular biology. Course is devoted primarily to reading, presentation, discussion, and critique of papers describing important recent research developments. Prerequisite:
CS 106A or equivalent, and an introductory course in biology or biochemistry. Recommended: some experience in mathematical modeling (does not need to be a formal course).
Terms: Win
| Units: 3
Instructors:
Dror, R. (PI)
BIOPHYS 279: Computational Biology: Structure and Organization of Biomolecules and Cells (BIOE 279, BIOMEDIN 279, CME 279, CS 279)
Computational techniques for investigating and designing the three-dimensional 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, cellular-level 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
Instructors:
Dror, R. (PI)
;
Bedi, R. (TA)
;
El-Gabalawy, O. (TA)
...
more instructors for BIOPHYS 279 »
Instructors:
Dror, R. (PI)
;
Bedi, R. (TA)
;
El-Gabalawy, O. (TA)
;
Fernandes, D. (TA)
;
Paggi, J. (TA)
;
Sanborn, A. (TA)
BIOPHYS 371: Computational Biology in Four Dimensions (BIOMEDIN 371, CME 371, CS 371)
Cutting-edge research on computational techniques for investigating and designing the three-dimensional structure and dynamics of biomolecules, cells, and everything in between. These techniques, which draw on approaches ranging from physics-based simulation to machine learning, play an increasingly important role in drug discovery, medicine, bioengineering, and molecular biology. Course is devoted primarily to reading, presentation, discussion, and critique of papers describing important recent research developments. Prerequisite:
CS 106A or equivalent, and an introductory course in biology or biochemistry. Recommended: some experience in mathematical modeling (does not need to be a formal course).
Terms: Win
| Units: 3
Instructors:
Dror, R. (PI)
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