1. BMI 205: P RECISION P RACTICE WITH B IG D ATA Daniel L. Rubin, MD, MS Associate Professor of Radiology and of Medicine (Biomedical Informatics) Department of Radiology Stanford University

2. 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

3. 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

4. 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”

5. 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 lecture https://med.stanford.edu/mediadropbox/cour seListing.html?identifier=biomedin205&cyyt =1152

6. 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.

7. Course website http://bmi205.stanford.edu/