Faculty member Serena Yeung teaches several DBDS and BMI courses on artificial intelligence and healthcare.
DBDS & BMI Courses
BIODS 48N: Riding the Data Wave, cross-listed as STATS 48N (Aut)
Instructors: Sabatti, Chiara
Imagine collecting a bit of your saliva and sending it in to one of the personalized genomics company: for very little money you will get back information about hundreds of thousands of variable sites in your genome. Records of exposure to a variety of chemicals in the areas you have lived are only a few clicks away on the web; as are thousands of studies and informal reports on the effects of different diets, to which you can compare your own. What does this all mean for you? Never before in history humans have recorded so much information about themselves and the world that surrounds them. Nor has this data been so readily available to the lay person. Expression as "data deluge'' are used to describe such wealth as well as the loss of proper bearings that it often generates. How to summarize all this information in a useful way? How to boil down millions of numbers to just a meaningful few? How to convey the gist of the story in a picture without misleading oversimplifications? To answer these questions we need to consider the use of the data, appreciate the diversity that they represent, and understand how people instinctively interpret numbers and pictures. During each week, we will consider a different data set to be summarized with a different goal. We will review analysis of similar problems carried out in the past and explore if and how the same tools can be useful today. We will pay attention to contemporary media (newspapers, blogs, etc.) to identify settings similar to the ones we are examining and critique the displays and summaries there documented. Taking an experimental approach, we will evaluate the effectiveness of different data summaries in conveying the desired information by testing them on subsets of the enrolled students.
BIODS 205: Bioinformatics for Stem Cell and Cancer Biology, cross-listed as STEMREM 205 (Wtr)
Instructor: Newman, Aaron
For graduate and medical students. High-throughput technologies and data science are essential tools in modern stem cell biology and cancer research. Students will gain practical exposure to bioinformatics concepts and techniques required to address biological questions within these research areas. The beginning of the quarter is focused on foundational principles underlying bioinformatics and genomics. Focus for the remainder of the quarter is on direct, hands-on experience with applications to common research problems. Topics include analysis of bulk and single-cell sequencing data, single gene to whole-genome analysis, machine learning, and data visualization. Intended for biology students without a background in computer science, or for students in a quantitative discipline interested in gaining exposure to key challenges in stem cell and cancer genomics. Basic programming experience is recommended but not required.
BIODS 215: Topics in Biomedical Data Science: Large-Scale Inference (Wtr)
Instructors: Rivas, Manuel; Salzman, Julia; Zou, James
The recent explosion of data generated in the fields of biology and medicine has led to many analytical challenges and opportunities for understanding human health. This graduate-level course focuses on methodology for large-scale inference from biomedical data. Topics include one-dimensional and multidimensional probability distributions; hypothesis testing and model comparison; statistical modeling; and prediction. This course will place a special emphasis on applications of these approaches to i) human genetic data; ii) hospital in-patient and health questionnaire data, which is increasingly available with the emergence of large precision initiatives like the UK Biobank and Precision Medicine Initiative; and iii) wearable and social network data.
BIODS 217: Analytics Accelerator, cross-listed as CME 217 (Aut, Wtr)
Instructors: Hansen, Kari; Rivas, Manuel
This is a multidisciplinary graduate level course designed to give students hands-on experience working in teams through real-world project-based research and experiential classroom activities. Students work in dynamic teams with the support of course faculty and mentors, researching preselected topics focused on COVID-19 during fall 2020 with the option to continue into winter 2021. Students apply a computational and data analytics lens and will use design thinking methodology. The course exposes students to ethics, emotional intelligence, unintended consequences of their work and team building supported by relevant lectures on data science and med/bio topics. Pre-requisites: none.nThe course application generally opens 5-6 weeks before registration for each quarter. If you missed the application for the quarter, please submit your application anyway to be added to the waitlist and to receive information regarding upcoming quarters.
BIODS 220: Artificial Intelligence for Healthcare, crosslisted as CS 271 and BIOMEDIN 220 (Aut)
Instructors: Yeung, Serena
Healthcare is one of the most exciting application domains of artificial intelligence, with transformative potential in areas ranging from medical image analysis to electronic health records-based prediction and precision medicine. This course will involve a deep dive into recent advances in AI in healthcare, focusing in particular on deep learning approaches for healthcare problems. We will start from foundations of neural networks, and then study cutting-edge deep learning models in the context of a variety of healthcare data including image, text, multimodal and time-series data. In the latter part of the course, we will cover advanced topics on open challenges of integrating AI in a societal application such as healthcare, including interpretability, robustness, privacy and fairness. The course aims to provide students from diverse backgrounds with both conceptual understanding and practical grounding of cutting-edge research on AI in healthcare.
BIODS 232: Consulting Workshop on Biomedical Data Science (Aut, Wtr, Spr)
Instructors: Lu, Ying; Sabatti, Chiara; Tian, Lu; Desai, Manisha; Efron, Bradley; Lavori, Phil; Narasimhan, Balasubramanian; Tamaresis, John
The Data Studio is a collaboration between Spectrum (The Stanford Center for Clinical and Translational research and Education) and the Department of Biomedical Data Science (DBDS). The educational goal of this workshop is to provide data science consultation training for students. Data Studio is open to the Stanford community, and we expect it to have educational value for students and postdocs interested in biomedical data science. Most sessions are workshops that provide an extensive and in-depth consultation for a Medical School researcher based on research questions, data, statistical models, and other material prepared by the researcher with the aid of our facilitator. At the workshop, the researcher explains the project, goals, and needs. Experts in the area across campus will be invited and contribute to the brainstorming. After the workshop, the facilitator will follow up,helping with immediate action items and summary of the discussion. The last session of each month is devoted to drop-in consulting. DBDS faculty are available to provide assistance with your research questions. Skills required of practicing biomedical consultants, including exposed to biomedical and health science applications, identification of data science related questions, selection or development of appropriate statistical and analytic approaches to answer research needs. Students are required to attend the regular workshops and participate one to two consulting projects as team members under the supervision of faculty members or senior staff. Depending on the nature of the consulting service, the students may need to conduct numerical simulation, plan sample size, design study, and analyze client data. the formal written report needs to be completed at the end of consulting projects. May be repeated for credit. Prerequisites: course work in applied statistics, data analysis, and consent of instructor.
BIODS 235: Brief Practices for Developing Data Science Software for Clinical and Healthcare Applications (Wtr)
Instructor: Bustamante, Carlos; De La Vega, Francisco.
Best practices for developing data science software for clinical and healthcare applications is a new seminar aimed to provide an overview of the strategies, processes, and regulatory hurdles to develop software implementing new algorithms or analytical approaches to be used in clinical diagnosis or medical practice. Upon completing this seminar, biomedical scientists implementing diagnostics, analytical, or AI-driven clinical decision support software should better understand how to protect, transfer, commercialize, and translate their inventions into the clinic. Topics include: Intellectual property strategies and technology licensing challenges; software development and quality best practices for the clinic; regulatory frameworks for clinical decision support and diagnostics informatics applications. It is open primarily to graduate students across Stanford and combines short lectures, guest industry speakers, and workshop sessions to allow participants to receive feedback on current related projects that are undertaking. Enrollment limited to 25 to allow participants present their current projects. Prerequisites: Basic experience in programing and algorithm or software tool development. Ideally, the participant is actively implementing a new method/process/application in software aimed to be used in the clinic.
BIODS 237: Deep Learning in Genomics and Biomedicine, crosslisted as CS 273B, BIOMEDIN 273B, and GENE 236 (Aut)
Instructors: Kundaje, Anshul; Zou, James
Recent breakthroughs in high-throughput 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 high-throughput 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.
BIODS 240: Race, Data Algorithms, and Health, cross-listed as BIOMEDIN 240 (Aut)
This course studies the interplay between race, data and algorithms in healthcare. The particular viewpoint we want to take is to understand the role of data, data analysis and algorithms in supporting equitable delivery of health care to members of all races. Topics as "representative data", "machine bias", "algorithmic fairness" are going to be central to the discussion. However, we want to stress the uniqueness of the "medicine/health care" viewpoint. For example, while in contexts as loan applications, it is normative that race information (or its proxies) not to be included among the variables used for decision, in healthcare, information on race is routinely collected in the attempt to provide "best" care. One of the goals of the class will be to understand what are the differences between biological populations and social environments that a doctor needs to be aware of as opposed to overly emphasized or imaginary ones. This will provide a context to understand the challenges that data collection and analysis faces to support equitable care.
Instructor: Sabatti, Chiara; Zou, James
BIODS 248: Clinical Trial Design in the Age of Precision Medicine and Health, cross-listed as BIODS 248P, BIOMEDIN 248 and STATS 248 (Spr)
Instructor: Lai, Tze
Overview of requirements, designs, and statistical foundations for traditional Phase I, II, and III clinical trials for medical product approval and Phase IV postmarketing studies for safety evaluation. As these methods cost too much and take too much time in the era of precision medicine and precision health, this course then introduces innovative designs that have been developed for affordable clinical trials, which can be completed within reasonable time constraints and which have been encouraged by regulatory agencies. Prerequisites: Working knowledge of statistics and R.
BIODS 253: Software Engineering For Scientists (Wtr)
Instructor: Teri Klein
Standardized Software Engineering techniques, such as cloud computing, virtualization, automated testing, and source control, are becoming increasingly critical for science and research. Software Engineering for Scientists is designed to help researchers, scientists, and non-domain-experts gain hands-on knowledge of the tools and practices that will make your day-to-day work more efficient and less error-prone, be it in academia or industry. Students will learn to adopt the most important and germane of the techniques used in the real world (from startups to large companies), and will provide you with a good understanding of the tools, approaches, and tradeoffs inherent in writing any kind of program. The class is taught by an expert with 20 years of experience building software, managing engineering and product teams at companies including Google and Twitter who now works primarily in biotechnology and a Professor in the Biomedical Data Sciences with more than 30 years of experience teaching in bioinformatics at both Stanford and UCSF. Since this class will be offered virtually, a recorded lecture (15-30 minutes long) will be made available before each class meeting. The lecture will cover one or more topics from both a theoretical and practical perspective. Students will be able to try to work on these techniques. Lectures will be interactive; we will spend time answering questions the students might have, and talking about how these learnings could be useful to their research.
BIODS 260A/B/C: Workshop in Biostatistics, crosslisted as STATS 260A/B/C (Aut, Wtr, Spr)
Instructors: Sabatti, Chiara; Yeung, Serena; Newman, Aaron; Rivas, Manuel; Palacios, Julia; Zou, James
Applications of statistical techniques to current problems in medical science. To receive credit for one or two units, a student must attend every workshop. To receive two units, in addition to attending every workshop, the student is required to write an acceptable one-page summary of two of the workshops, with choices made by the student.
BIODS 388: Stakeholder Competencies for Artificial Intelligence in Healthcare, cross-listed as BIOMEDIN 388(Aut)
Instructor: Yeung, Serena
Advancements of machine learning and AI into all areas of medicine are now a reality and they hold the potential to transform healthcare and open up a world of incredible promise for everyone. But we will never realize the potential for these technologies unless all stakeholders have basic competencies in both healthcare and machine learning concepts and principles - this will allow successful, responsible development and deployment of these systems into the healthcare domain. The focus of this course is on the key concepts and principles rather than programming or engineering implementation. Those with backgrounds in healthcare, health policy, healthcare system leadership, pharmaceutical, and clinicians as well as those with data science backgrounds who are new to healthcare applications will be empowered with the knowledge to responsibly and ethically evaluate, critically review, and even use these technologies in healthcare. We will cover machine learning approaches, medical use cases in depth, unique metrics to healthcare, important challenges and pitfalls, and best practices for designing, building, and evaluating machine learning in healthcare applications.
BIODS 472: Data Science and AI for COVID-19, cross-listed as BIOMEDIN 472 and CS472 (last offered Spring 2o19)
Instructor: Zou, James
This project class investigates and models COVID-19 using tools from data science and machine learning. We will introduce the relevant background for the biology and epidemiology of the COVID-19 virus. Then we will critically examine current models that are used to predict infection rates in the population as well as models used to support various public health interventions (e.g. herd immunity and social distancing). The core of this class will be projects aimed to create tools that can assist in the ongoing global health efforts. Potential projects include data visualization and education platforms, improved modeling and predictions, social network and NLP analysis of the propagation of COVID-19 information, and behavior-nudging tools. The class is aimed toward students with experience in data science and AI, and will include guest lectures by biomedical experts. Prerequisites: background in machine learning and statistics ( CS229, STATS216 or equivalent). Some biological background is helpful but not required.
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
Instructors: Plevritis, S. (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 | Units: 1
Instructors: Rubin, D. (PI)
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
Instructors: Li, R. (PI); Morse, K. (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: Aut, Win, Spr, Sum | Units: 1
Instructors: Stevens, L. (PI) ; Chen, J. (SI)
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.
Terms: Aut, Win, Spr, Sum | Units: 1-6 | Repeatable for credit
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: 1-18 | Repeatable for 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: 4-18 | Repeatable for credit
BIOMEDIN 390A / 390B / 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 follow-up on projects they expect to perform. BIOMEDIN 390A, B, and C may each be taken only once.
Terms: Aut, Win, Spr, Sum | Units: 1
Instructors: Musen, M. (PI) ; Tian, L. (PI)
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
BIOMEDIN 802: TGR PhD Dissertation
Terms: Aut, Win, Spr, Sum | Units: 0 | Repeatable for credit
BIOMEDIN 210: Modeling Biomedical Systems: Ontology, Terminology, Problem Solving (CS 270)
Methods for modeling biomedical systems and for building model-based 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 hands-on experience with several systems and tools. Prerequisites: CS106A, basic familiarity with biology, probability, and logic.
Terms: Win | Units: 3
Instructors: Musen, M. (PI)
BIOMEDIN 212: Introduction to Biomedical Informatics Research Methodology (BIOE 212, CS 272, GENE 212)
Capstone Biomedical Informatics (BMI) 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.
Terms: Spr | Units: 3-5
Instructors: Plevritis, S. (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, 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.
(For lectures only, please register for BIOMEDIN 216)
Terms: Aut | Units: 3-4
Instructors: Altman, R. (PI)
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 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
(For lectures only, please register for BIOMEDIN 225)
Terms: Aut | Units: 3
Instructors: Shah, N. (PI)
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.
(For lectures only, please register for BIOMEDIN 218)
Terms: Aut | Units: 4
Instructors: Wall, D. (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. (SI)
BIODS 220 / BIOMEDIN 220: Artificial Intelligence in Healthcare (CS 271)
Healthcare is one of the most exciting application domains of artificial intelligence, with transformative potential in areas ranging from medical image analysis to electronic health records-based prediction and precision medicine. This course will involve a deep dive into recent advances in AI in healthcare, focusing in particular on deep learning approaches for healthcare problems. We will start from foundations of neural networks, and then study cutting-edge deep learning models in the context of a variety of healthcare data including image, text, multimodal and time-series data. In the latter part of the course, we will cover advanced topics on open challenges of integrating AI in a societal application such as healthcare, including interpretability, robustness, privacy and fairness. The course aims to provide students from diverse backgrounds with both conceptual understanding and practical grounding of cutting-edge research on AI in healthcare.
Terms: Win | Units: 3-4
Instructors: Yeung, S. (PI)
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 multi-omics, multi-modal and multi-scale data fusion of biomedical data in the context of biomedical decision support. Prerequisites: CS106A or equivalent, Stats 60 or equivalent.
Terms: Aut | Units: 2
Instructors: Gentles, A. (PI) ; Gevaert, O. (PI)
BIOMEDIN 222: Cloud Computing for Biology and Healthcare (CS 273C, GENE 222)
Big Data is radically transforming healthcare. To provide real-time personalized healthcare, we need hardware and software solutions that can efficiently store and process large-scale biomedical datasets. In this class, students will learn the concepts of cloud computing and parallel systems' architecture. This class prepares students to understand how to design parallel programs for computationally intensive medical applications and how to run these applications on computing frameworks such as Cloud Computing and High Performance Computing (HPC) systems. Prerequisites: familiarity with programming in Python and R.
Terms: Spr | Units: 3
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
Instructors: Altman, R. (PI) ; Carrillo, M. (SI) ; Costa, H. (SI) ; Khuri, N. (SI)
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 | Units: 2-3
Instructors: Chan, A. (PI) ; Musen, M. (SI)
BIOMEDIN 248: Clinical Trial Design in the Age of Precision Medicine and Health (BIODS 248, STATS 248)
Overview of requirements, designs, and statistical foundations for traditional Phase I, II, and III clinical trials for medical product approval and Phase IV postmarketing studies for safety evaluation. As these methods cost too much and take too much time in the era of precision medicine and precision health, this course then introduces innovative designs that have been developed for affordable clinical trials, which can be completed within reasonable time constraints and which have been encouraged by regulatory agencies. Prerequisites: Working knowledge of statistics and R.
(SCPD students should register for BIODS 248P)
Terms: Spr | Units: 3
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 hands-on data analysis and application of methods, including re-analyses of well-known studies. Prerequisites: one or more courses in probability, and statistics or biostatistics.
Terms: Spr | Units: 4
Instructors: Bendavid, E. (PI) ; Bhattacharya, J. (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 249. Prerequisites: ECON 50 and either ECON 102A or STATS 116 or the equivalent. Recommended: ECON 51.
Terms: Spr | Units: 5
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 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
Instructors: Rubin, D. (PI)
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), control-flow logic (gene regulation) and run-time 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
Instructors: Bejerano, G. (PI)
BIOMEDIN 273B: Deep Learning in Genomics and Biomedicine (BIODS 237, CS 273B, GENE 236)
Recent breakthroughs in high-throughput 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 high-throughput 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. Stude more »
Terms: Aut | Units: 3
Instructors: Kundaje, A. (PI) ; Zou, J. (PI)
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)
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
Instructors: Owens, D. (PI) ; Salomon, J. (PI)
BIOMEDIN 472: Data science and AI for COVID-19 (BIODS 472, CS 472)
This project class investigates and models COVID-19 using tools from data science and machine learning. We will introduce the relevant background for the biology and epidemiology of the COVID-19 virus. Then we will critically examine current models that are used to predict infection rates in the population as well as models used to support various public health interventions (e.g. herd immunity and social distancing). The core of this class will be projects aimed to create tools that can assist in the ongoing global health efforts. Potential projects include data visualization and education platforms, improved modeling and predictions, social network and NLP analysis of the propagation of COVID-19 information, and behavior-nudging tools. The class is aimed toward students with experience in data science and AI, and will include guest lectures by biomedical experts. Prerequisites: background in machine learning and statistics ( CS229, STATS216 or equivalent). Some biological background is helpful but not required.
Terms: Spr | Units: 2