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--- biomedin215 [2020/06/24 16:25]
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+++ biomedin215 [2023/09/01 14:42] (current)
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-====== BIOMEDIN 215 DATA DRIVEN MEDICINE ======
+====== BIOMEDIN 215: Data Science for Medicine (Aut 2023) ======
+The Autumn 2023 class will be offered in person.
+– 2023 Teaching team: Nigam Shah, Alison Callahan, Bryan Bunning, Maggie Wang, Ben Viggiano, Yixing Jiang
+This course is designed to prepare you to pose and answer meaningful clinical questions using routinely collected healthcare data. The practical skills you will learn in this class will be applicable to any task involving data manipulation and analysis. The course will use real, de-identified, large size patient datasets for home work projects associated with the course. To have the best learning experience, you will need to be proficient in the R language.
+Upon completing this course, you should be able to:
+  - recognize categories of research questions and the study designs used to address them.
+  - describe common healthcare data sources and their relative advantages and limitations.
+  - extract and transform various kinds of clinical data to create analysis-ready datasets.
+  - design and execute an analysis of a clinical dataset to answer a research question.
+  - apply your knowledge to evaluate and criticize published clinical informatics research.
+Schedule: TUE, THU 1:30 PM - 2:50 PM in Gates B3. \\
+For Office Hours, see the [[https://canvas.stanford.edu/courses/178048 | Canvas site]] (log in with your SUNet id) \\
+Pre-requisites: See the [[https://docs.google.com/document/d/1se4LuFfeIpZBzgiGL5AM57jg_dBOCG37nNWdIfEoYZQ/edit|syllabus]]
+<html>
+<iframe width='900' height='580' frameborder='0's src="https://docs.google.com/spreadsheets/d/e/2PACX-1vSUt9Hf34dkX_ioxJugITzOo_HhqbRC2kXTEW6auFRvAsxQzxxmRFPI3Ued8SuFGCIHuBWRwxGz7EGL/pubhtml?gid=34616104&single=true&widget=true&headers=false"></iframe></html> //
-In the Autumn quarter of 2020, this class will be very different compared to the [[biomedin215_precovid|pre-COVID]] offering and will be limited to graduate students in the Biomedical Informatics Program.

Shah Lab

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