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1 - 10 of 15 results for: BIOMEDIN ; Currently searching winter courses. You can expand your search to include all quarters

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BIOMEDIN 201: Biomedical Data Science Student Seminar (BIODS 201)

Participants report on their research projects or share a review of recent articles from biomedical data science literature. The goals are (1) to practice oral communications and effective presentations to an audience not familiar with their particular research area, and (2) to learn more about other research across biomedical data science that may be relevant to their work.
Terms: Aut, Win, Spr | Units: 1 | Repeatable 3 times (up to 3 units total)

BIOMEDIN 202: An overview of 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. Basic familiarity with the following concepts & skills will be necessary for completing the problem sets: A crash course on commonly used bioinformatics tools (see BIOS 201), A theoretical exploration of bioinformatics algorithms (see BIOMEDIN 214), an introduction to programming/Python/Linux (see BIOS 201, BIOS 205, CS 106A, etc.), an intro to statistics and probability (see STATS 116, STATS 200). If you have any questions about whether this course is the right level for you, please speak with a TA or instructor. 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: An overview of 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. Basic familiarity with the following concepts & skills will be necessary for completing the problem sets: A crash course on commonly used bioinformatics tools (see BIOS 201), A theoretical exploration of bioinformatics algorithms (see BIOMEDIN 214), an introduction to programming/Python/Linux (see BIOS 201, BIOS 205, CS 106A, etc.), an intro to statistics and probability (see STATS 116, STATS 200). If you have any questions about whether this course is the right level for you, please speak with a TA or instructor. 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 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 | Units: 3
Instructors: Musen, M. (PI)

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 decision-making techniques, Markov models of dynamic disease state problems, utility assessment techniques, parametric utility models, utility models for multidimensional outcomes, analysis of time-varying clinical outcomes, and the design of cost-constrained clinical policies. Extensive problem sets compliment the lectures. Prerequisites: introduction to calculus and basic statistics.
Terms: Win | Units: 3
Instructors: Higgins, M. (PI) ; Musen, M. (PI)

BIOMEDIN 233: Intermediate Biostatistics: Analysis of Discrete Data (EPI 261, STATS 261)

Methods for analyzing data from case-control and cross-sectional studies: the 2x2 table, chi-square test, Fisher's exact test, odds ratios, Mantel-Haenzel methods, stratification, tests for matched data, logistic regression, conditional logistic regression. Emphasis is on data analysis in SAS or R. Special topics: cross-fold validation and bootstrap inference.
Terms: Win | Units: 3
Instructors: Baiocchi, M. (PI) ; Liang, R. (TA)

BIOMEDIN 290: Biomedical Informatics Teaching Methods

Hands-on training in biomedical informatics pedagogy. Practical experience in pedagogical approaches, variously including didactic, inquiry, project, team, case, field, and/or problem-based approaches. Students create course content, including lectures, exercises, and assessments, and evaluate learning activities and outcomes. Prerequisite: instructor consent.Please enroll in your instructors section, if its not available, please reach out to Asma'a Eljibahi (ajibahi@stanford.edu) for assistance.
Terms: Aut, Win, Spr, Sum | Units: 1-6 | Repeatable 2 times (up to 12 units total)

BIOMEDIN 299: Directed Reading and Research

For students wishing to receive credit for directed reading or research time. Prerequisite: consent of instructor. Please enroll in your instructors section, if its not available, please reach out to Asma'a Eljibahi (ajibahi@stanford.edu) for assistance.
Terms: Aut, Win, Spr, Sum | Units: 1-18 | Repeatable for credit
Instructors: Aghaeepour, N. (PI) ; Alarid Escudero, F. (PI) ; Alsentzer, E. (PI) ; Altman, R. (PI) ; Ashley, E. (PI) ; Baiocchi, M. (PI) ; Bassik, M. (PI) ; Bayati, M. (PI) ; Bejerano, G. (PI) ; Bendavid, E. (PI) ; Bhattacharya, J. (PI) ; Blish, C. (PI) ; Boahen, K. (PI) ; Brandeau, M. (PI) ; Bustamante, C. (PI) ; Chang, H. (PI) ; Chaudhari, A. (PI) ; Chen, J. (PI) ; Cherry, J. (PI) ; Cohen, S. (PI) ; Covert, M. (PI) ; Daneshjou, R. (PI) ; Das, R. (PI) ; Davis, R. (PI) ; Delp, S. (PI) ; Desai, M. (PI) ; Dror, R. (PI) ; Engelhardt, B. (PI) ; Feldman, M. (PI) ; Ferrell, J. (PI) ; Fraser, H. (PI) ; Gentles, A. (PI) ; Gevaert, O. (PI) ; Greenleaf, W. (PI) ; Guibas, L. (PI) ; Hastie, T. (PI) ; Hernandez-Boussard, T. (PI) ; Hlatky, M. (PI) ; Holmes, S. (PI) ; Jerby, L. (PI) ; Ji, H. (PI) ; Khatri, P. (PI) ; Kirkegaard, K. (PI) ; Klein, T. (PI) ; Koller, D. (PI) ; Kundaje, A. (PI) ; Langlotz, C. (PI) ; Leskovec, J. (PI) ; Levitt, M. (PI) ; Li, J. (PI) ; Lu, Y. (PI) ; Mallick, P. (PI) ; Manning, C. (PI) ; Menon, V. (PI) ; Montgomery, S. (PI) ; Musen, M. (PI) ; Napel, S. (PI) ; Nolan, G. (PI) ; Owen, A. (PI) ; Owens, D. (PI) ; Palacios, J. (PI) ; Palaniappan, L. (PI) ; Pande, V. (PI) ; Petrov, D. (PI) ; Plevritis, S. (PI) ; Pohl, K. (PI) ; Poldrack, R. (PI) ; Pritchard, J. (PI) ; Relman, D. (PI) ; Rivas, M. (PI) ; Rose, S. (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) ; Tibshirani, R. (PI) ; Utz, P. (PI) ; Wall, D. (PI) ; Witte, J. (PI) ; Wong, W. (PI) ; Xing, L. (PI) ; Yeung, S. (PI) ; Zou, J. (PI)

BIOMEDIN 360: Inclusive Mentorship in Data Science (BIODS 360)

This course has the following broad goals: (1) To ensure that Stanford graduate students in data science are intentionally trained to effectively mentor people who may be different from them. (2) To sustainably develop pathways to increase access to higher education and to Stanford graduate programs in data science for individuals from backgrounds currently under-represented in those fields. During weekly class meetings, graduate student participants will learn strategies to create an inclusive environment, approaches to effective mentoring and coaching, and techniques to develop a personalized curriculum with the course staff and guest speakers. They will also be paired with current undergraduates from non-R1 schools with an interest in data science, recruited in partnership with faculty from those institutions. Participants will meet online weekly for one-on-one mentorship where you will expose your mentee to research in data science. During weekly online meetings, you will work with your mentee on a range of activities, planned with assistance from course staff, including planning their course of studies, navigating internship opportunities and preparing applications; tutoring in some aspects of data science; and guidance in engaging in mini-research projects, depending on their interests.
Terms: Win | Units: 1-2 | Repeatable 2 times (up to 4 units total)
Instructors: Sabatti, C. (PI)

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: 4-18 | Repeatable for credit
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