Methods Track: Biomedical Natural Language Processing

Biomedical Natural Language Processing (NLP) blends skills in algorithm development with practical knowledge of applications of NLP in biomedicine and health care, biomedical knowledge resources, and characteristics of biomedical text. Students will demonstrate competency in (a) implementing natural language processing and machine learning algorithms to solve problems in the biomedical and health domains, (b) processing text data from different genres including clinical narrative, social media, and the biomedical literature, (c) creating reference standard datasets for evaluating NLP performance, and (d) integrating NLP tools with biomedical informatics applications, such as clinical decision support, surveillance, and literature-based discovery.

Recommended Course of Study

This is a recommended schedule for this track. Courses can be waived or tested out of with permission of the course instructor and the student's advisor. Ultimately, the courses a student takes should be determined and approved by the student and the graduate committee.

Year/Semester		Course	Cr.
Year 1 Fall	BMI 6018	Intro to Computer Programming	3
	BMI 6010	Fnd Healthcare Informatics	2
	BMI 6701	Population and Public Health	2
	BMI 6030	Fnds Bioinformatics	2
		Ethics	1
Year 1 Spring	BMI 6106	Into to Probability and Statistics	3
	BMI 6111	Research Design Part I	1.5
	BMI 6103	Biomedical Text Processing	2
	BMI 6120	Standards in Biomedical Informatics	2
	BMI 6112	Research Design Part II	1.5
Year 2 Fall	CS 6340	Natural Language Processing	3
	BMI 6050	Applied Machine Learning	4
	BMI 6203	Clinical Database Design II	2
	BMI 6103	Systems Modeling and Process Improvement	2
Year 2 Spring		Electives
		Research Credits
Year 3 Fall	CS 6350	Machine Learning	3
		Electives
		Research Credits

Recommended Electives

CS 6140	Data Mining
CS 7935	NLP Seminar
CS 6530	Database Systems
IS 6482	Data Mining
IS 6483	Adv. Data Mining
IS 6580	Data Science and Big Data
BMI 6300	Medical Decision Making and Knowledge Engineering
CS 6390	Information Extraction from Text
CS 6300	Artificial Intelligence
IS 6910	Data Mining in Healthcare
CS 6630	Visualization
IS 6480	Data Warehouse Design and Implementation
OIS 6040	Data Analysis and Decision Making

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
Semantic Interoperability SIG
Decision-making and Behavioral Informatics SIG

Practicum

Sign up for at least one practicum to gain hands-on experience and work with a team on a project.

Data Science Scholars--Applied NLP

This practicum is an extended research-in-progress course for natural language processing. Students in this course will either lead their own project as a data science scholar or participate in an existing project applying NLP to a health-related problem. The course offers 1) mentored guidance and peer review for your NLP project and 2) the opportunity to present research results, manuscript drafts, and grant proposals ideas to peers, mentors, and informatics faculty for feedback. Students not leading an NLP project will have ample opportunity to work on an NLP team and participate in an existing project.