BMI 205: Precision Practice with Big Data

Daniel L. Rubin, MD, MS

Associate Professor of Radiology and of Medicine (Biomedical Informatics)

Department of RadiologyStanford 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• Time: Wednesdays 12:30-1:20 pm, lunch

provided for registered students.• Videos: Recordings will be posted after each

lecturehttps://med.stanford.edu/mediadropbox/courseListing.html?identifier=biomedin205&cyyt=

1152

Course administration• Units: 1 unit• TA: Manoj Sharma

(manojs@stanford.edu)• Requirements: Weekly attendance

– If 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 manojs@stanford.edu with BMI-205 at the beginning of the subject line.

Course websitehttp://bmi205.stanford.edu/