BMI 205: P RECISION P RACTICE WITH B IG D ATA 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 19 th 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 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 with BMI205 at the beginning of the subject line.

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