Data Science, Analytics, AI and Computational Methods
Data Science, Analytics, AI and Computational Methods teaches knowledge and skills of data analytics and computational approaches in healthcare. Students must demonstrate competency in understanding and applying (a) high-level mathematical analysis of large data sets, (b) predictive and prescriptive analytic methods and tools, (c) NLP, (d) optimal clinical knowledge management principles at healthcare organizations, and (e) information visualization. Students with interest in extended training in Big Data analytics may concurrently take courses in the School of Computing’s Big Data Certificate program. While students are not expected to take every course, they are expected to gain specialized knowledge in key computational methodologies. For example, trainees may specialize in NLP and predictive analytics, where the courses blend skills in algorithm development with practical knowledge of applications of NLP in biomedicine and healthcare, biomedical knowledge resources, and characteristics of biomedical text. This enables trainees to implement NLP solutions.
Special Interest Groups
PhD students are required to attend at least one SIG (all students are encouraged), which bring people with similar interests together to learn, share, and experiment.
- Computational SIG
- Decision-making and Behavioral Informatics SIG
- Semantic Interoperability SIG
Sign up for at least one practicum to gain hands-on experience and work with a team on a project.
Students have the opportunity to apply to work for a semester with the ReImagineEHR team or the Sociotechnical expertise on an existing project. Must be coordinated with the team/expertise director before registering:
Review and discuss literature in this area.
If there is not a current journal club, encourage your SIG to host one.
Feel free to meet individually with faculty and/or to attend their weekly lab meetings.
Samir Abdelrahman; Bruce Bray; Mollie Cummins, NI; Jennifer Garvin, VA; Guilherme Del Fiol; Scott Evans, Intermountain; Julio Facelli; Bryan Gibson; Peter Haug, Intermountain; Kensaku Kawamoto; Gang Luo; Scott Narus, Intermountain; Dennis Parker; Catherine Staes; Kathy Sward, NI; Charlene Weir, VA.
Laura Heermann-Langford, Nursing Informatics, Intermountain; Jeff Phillips, School of Computing Big Data Science; Heather Sobkho, 3M.