AISC610.0

Computationally-Enabled Medicine

Credits: 4 CREDITS (2P+2B)
Sites: HMS and Clinical Sites
Director(s): Paul Avillach, Isaac Kohane
Offered: March
Location: HMS - Harvard Medical School (0)
Open to Exclerks: No (HMS only)
Description: Summary: Computational approaches to analyzing large data sets and applying the insights derived to clinical decision-making are central to the present and future of biomedicine. This course will enable students to acquire a computational framework and toolkit for addressing this growing analytic challenge. Selected examples from genomics clinical decision-making and from epidemiology informed by "big data" obtained from electronic healthcare data, claims data and even the social web will serve as the basis for exploration of the computational framework. Mentored experiences at medical data science companies and state-of-the-art clinical diagnostics enterprises will enable students to experience the application of and elaborate questions that can be addressed by computational biomedicine. Format of classroom-based sessions: Classroom sessions are generally divided in two parts. The first part will include journal article presentations by students, with an accompanying discussion led by the course directors. The second part of class will be devoted to completing in-class assignments where students will apply and develop their R programming skills to the computational problems that were discussed in class with individual guidance from teaching assistants or directors. Four sessions will be led by outside lecturers. Format of clinical/field experiences: Two mornings per week (working on a data project, mentored by individuals at a health tech company (a pharmaceutical company, Aetna or computing research company). Anticipated schedule: All AISCs are full time courses. Students are expected to devote at least 40 hours per week to scheduled sessions and preparatory work. This course will consist of three half-days per week in classroom sessions (Monday, Wednesday and Friday mornings), an additional two 2-hour sessions per week to complete homework assignments using R programming skills (Tuesday and Thursday afternoons), and two half-days per week at a health tech company (Tuesday and Thursday mornings). Effective learning in this course will require that students have prior knowledge in the R programming language. Students missing this background should take the short & effective online course https://www.edx.org/course/data-science-r-basics before March 2020.