Download Proposed System Architecture - BMI 205

BMI 205: PRECISION
PRACTICE WITH BIG DATA
Daniel L. Rubin, MD, MS
Associate Professor of Radiology,
of Medicine (Biomedical Informatics),
and of Biomedical Data Science
Department of Radiology
Stanford University
Outline
• Course information
• Introduction: Big Data and precision practice
• Big Data challenge: Decision making in cancer
treatment
• Big Data solution: “Learning Healthcare
Systems” in cancer
• Conclusion
Course goals (1)
• Show how medical practice and research are
being transformed by large amounts of data
(clinical, molecular, imaging)
• Show how computer methods can enable
precision care
– Help physicians recognize the best therapy
– Get the knowledge they need when they need it
– Discover new knowledge and challenge
established dogma
– Broaden clinical decision making beyond just
published knowledge and physician experience
Course goals (2)
• Some major topics illustrated
– Disease sub-typing/patient profiling
– Data mining
– Predicting treatment response
– Personalized treatment
– Getting computers to work with unstructured data
(text and images)
– The “Learning Healthcare System”
Course administration
• Location: LKSC, Room 120
– * Please note October 19th class will be in LKSC
130 *
• Time: Wednesdays 12:30-1:20pm, lunch will
be provided and served at 12:00pm.
• Videos: Recordings will be posted after each
lecture
Course administration
• Units: 1 unit
• TA: Alice Yu
([email protected])
• Requirements: Weekly attendance
– If you miss a session, view recorded seminar and
complete a short written assignment.
– The assignment will be posted shortly after lecture
and due prior to the next scheduled talk.
– Submit to [email protected] with
BMI205 at the beginning of the subject line.
Course website
http://bmi205.stanford.edu/