Course Description
This course covers the foundations and applications of data science for medical decision making using predictive modeling. Computational Medicine (CM) is an emerging discipline that seeks to: develop mechanistic computational models of disease; methods for personalizing these models using data measured from individual patients; and applying these personalized models to improve the diagnosis and treatment of disease.
Learn from faculty members in the Johns Hopkins Institute for Computational Medicine (ICM), who believe that CM offers one pathway to precision medicine. They are committed to training the next generation of scientists, engineers, and physicians in this approach, and doing this across all educational levels.
Class time includes lectures and tutorials covering the physiology, medicine, and engineering principles relevant to case studies that will be presented at the end of the course.
Key Takeaways
By the end of this course, you will be able to:
Describe foundations of predictive modeling.
Leverage data and personalized models to reveal and identify clinical states or phenotypes.
Assess risk of adverse events using models.
Develop predictive models to determine response to treatment.
Apply techniques to deal with challenges in real data sets.
Describe real-world applications through case studies.
Build your own models via a hands-on tutorial – learners will build personalized models to solve a clinical problem based on real world data.
Describe how the tools and techniques of data science are becoming integrated into the practice of medicine.
Prerequisites
Helpful to have background in probability and statistics.
Programming background is a plus, but not required.
Who Should Take this Course
Policies
- Johns Hopkins Engineering Lifelong Learning courses do not offer academic credit.
- Learners will have view only access to online materials for 1 year after the conclusion for the course.
- Additional policies are available on the Johns Hopkins Lifelong Learning Policies page.
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