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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
    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.


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.

Journal Clubs

Review and discuss literature in this area.

NLP Journal Club

Affiliated Faculty

Feel free to meet individually with faculty and/or to attend their weekly lab meetings.


Samir AbdelRahman; Michael Conway; Jeffrey Ferraro, Intermountain; Peter Haug, Intermountain; John Hurdle, Olga Patterson, Epidemiology.


Ellen Riloff, Computer Science; Vivek Srikumar, Computer Science.

PhD Application Deadlines

December 31st

It is advantageous to submit your application as soon as possible.  We will begin reviewing applications December 1, 2022.

Online Application

Biomedical Informatics at the University of Utah