XBIOMEDIN215 Machine Learning Projects in Healthcare
Quarter: Winter 2023
Solve real-world healthcare challenges using machine learning. Modeled after the popular BIOMEDIN215 Stanford graduate course, this professional course explores the unique data challenges of the healthcare industry and how machine learning can be applied to help solve them. In this course, we introduce methods for using large-scale electronic medical records data for machine learning, applying text mining to medical records, and for using ontologies for the annotation and indexing of unstructured content as well as for intelligent feature engineering.
Throughout the course, you will work through interactive exercises and case studies, attend live webinars from Stanford faculty and guest speakers, receive ongoing feedback from our course team, and collaborate with your fellow learners. Gain the real-world skills you need to run your own machine learning projects in industry.
- Work with healthcare data and how to use data to conduct research studies
- Differentiate between categories of research questions and the study designs used to address them
- Describe common healthcare data sources and their advantages and limitations relative to different research questions
- Extract and transform various kinds of clinical data to create analysis-ready datasets
- Execute tasks involving data manipulation and analysis
BIODS220 Artificial Intelligence in Healthcare
Quarter: Fall 2021
BIODS 220 will be offered again this fall. A major focus of the course is on deep learning algorithms for various types of healthcare data, and a significant part of this is a quarter-long project using deep learning for a healthcare application. Ideas for projects are currently being accepted - please see this Google form for more information, and to enter potential project ideas. To access the form, you will need to be logged into Google using your Stanford email address. If you have multiple ideas, submit one form per project. Mentorship can range from occasional meetings / feedback to working closely with the students - you can specify the level in the form. Interested students will contact you directly to explore mutual interest.
Please submit any project ideas by Saturday, September 25, 2021. Note that data needed for any projects should be available by this time. Please contact email@example.com for any questions.
ICME Summer Workshops 2021 | Fundamentals of Data Science
Quarter: Summer 2021
ICME's (Institute for Computational & Mathematical Engineering) 6th annual Summer Workshop Series will offer a variety of virtual data science and AI courses, taught live via Zoom by world-renowned Stanford faculty and Stanford-affiliated instructors. Discounts are offered to students, staff, and faculty from all schools. Discounts also apply to ICME industry partners.
The series offer:
- New and Intermediate workshops such as Data Privacy and Ethics, Intermediate Topics in Machine Learning and Deep Learning, and Deep Learning for Natural Language Processing - Part II.
- Thirteen workshops over three weeks, from August 2-20, 2021.
- Half-day workshops (from either 8-11 am or 1-4 pm Pacific time) spread over two days.
Registered participants can earn a “Certificate of Completion” from Stanford ICME after completing four or more workshops this summer.
Biomedin 260 Computational Methods for Biomedical Image Analysis and Interpretation
Quarter: Each Spring
Beginning with the latest biological and medical imaging modalities and their applications in research and medicine, this class focuses 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. With the permission of the instructor, students may enroll for 3 units and participate with reduced project requirements.
CS 523 Research Seminar in Computer Vision and Healthcare
Quarter: Spring 2021
With advances in deep learning, computer vision (CV) has been transforming healthcare, from diagnosis to prognosis, from treatment to prevention. Its far-reaching applications include surgical assistants, patient monitoring, data synthesis, and cancer screening. Before these algorithms make their way into the clinic, however, there is exciting research to develop methods that are accurate, robust, interpretable, grounded, and human-centered. In this seminar, we deeply examine these themes in medical CV research through weekly intimate discussions with researchers from academia and industry labs who conduct research at the center of CV and healthcare. Each week students will read and prepare questions and reflections on an assigned paper authored by that week's speaker. We highly encourage students who are interested in taking an interactive, deep dive into medical CV research literature to apply. While there are no hard requirements, we strongly suggest having the background and fluency necessary to read and analyze AI research papers (thus MATH 51 or linear algebra, and at least one of CS 231x, 224x, 221, 229, 230, 234, 238, AI research experience for CV and AI fundamentals may be helpful). Interest form/application can be filled out here.
CS 106A Code in Place
Quarter: Spring 2021
CS106A is one of most popular courses at Stanford University, taken by almost 1,600 students every year. It has been developed over the last 30 years by an amazing team, including Nick Parlante, Eric Roberts and more. The course teaches the fundamentals of computer programming using the widely-used Python programming language. This course is for everyone from humanists, social scientists, to hardcore engineers. Code in Place is built off the first half of CS106A.
Code in Place require no previous background in programming — just a willingness to work hard and a love for learning. It requires considerable dedication and hard work, over a course of 5 weeks.
MED 18SI Artificial Intelligence in Medicine and Healthcare Ventures
Quarter: Spring 2021
The face of healthcare is changing - innovative technologies, based on recent advances in artificial intelligence, are radically altering how care is delivered. Startups are offering entirely new ways to diagnose, manage, treat, and operate. Few ever reach the patient - those that do have much more than an idea and an algorithm; they have an intimate understanding of the healthcare landscape and the technical knowhow to successfully integrate AI solutions into the medical system. In this course, we tackle the central question: How can young students find feasible and impactful medical problems, and build, scale, and translate technology solutions into the clinic. Together, we will discover the transformative technologies of tomorrow that we can build today. Please see the syllabus for more information. We encourage students of all backgrounds to enroll- the only prerequisite is a strong passion for technology in healthcare.
BIODS 388 / BMI 388 / MED 288 Stakeholder Competencies for Artificial Intelligence in Healthcare
Quarter: Fall 2020
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.
CS 522 Seminar in Artificial Intelligence in Healthcare
Quarter: Fall 2020
Artificial intelligence is poised to make radical changes in healthcare, transforming areas such as diagnosis, genomics, surgical robotics, and drug discovery. In the coming years, artificial intelligence has the potential to lower healthcare costs, identify more effective treatments, and facilitate prevention and early detection of diseases. This class is a seminar series featuring prominent researchers, physicians, entrepreneurs, and venture capitalists, all sharing their thoughts on the future of healthcare. We highly encourage students of all backgrounds to enroll (no AI/healthcare background necessary). Speakers and more at CS522 Seminar in AI in Healthcare.