Biomedical Informatics Concentration

Concentration Courses

A student in the area of Biomedical Informatics can choose a concentration by completing courses as outlined below. You need to be enrolled in a GVSU master’s degree program. Concentration candidates enroll in the standard master’s degree program courses, with grading criteria being identical. The Biomedical Informatics concentration requires a 3-course sequence, which totals 9 credit hours:

  1. CIS 661 - Introduction to Medical and Bioinformatics Credits: 3
  2. Electives (choose 2):

The following courses are offered as online hybrid classes (also as part of the certificate):


Course Descriptions

CIS 661 - Introduction to Medical and Bioinformatics

A survey of fundamental concepts of medical and bioinformatics methods and techniques involved in the integration of computer systems in medical centers and life science industries.  Introduction to biomedical information systems; data representation, modeling, management and mining; systems evaluation; project management practices for biomedical decision making. Legal and ethical considerations. Offered fall and winter semesters.
Credits: 3

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CIS 665 - Clinical Information Systems

Historical development of clinical information systems, including hospital information systems and community health information systems. Topics covered include: clinical information systems and medical informatics, components of clinical information systems, examples of clinical information systems. Offered fall semester. Prerequisite: CIS 661.
Credits: 3

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CIS 677 - High-performance Computing


Introduction to parallel and high-performance computing. Coverage includes modern scalable parallel and distributed architectures, design and analysis of algorithms, communication and synchronization issues, software development environments, and performance evaluation. Case studies include applications in bioinformatics, evolutionary computing, data mining of biological and clinical databases, and knowledge-based systems. Offered fall semester. Prerequisite: CIS 500 or equivalent.

Credits: 3

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CIS 678 - Machine Learning

Broad introduction to machine learning computer programs that improve their performance with experience. Topics include decision trees, neural networks, statistical methods, genetic algorithms, Bayesian learning methods, explanation-based goal regression, reinforcement learning, and learning frameworks. Includes an applied machine learning component that provides exposure to established algorithms and machine learning programs. Offered winter semester. Prerequisite: CIS 500 or equivalent.
Credits: 3

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Page last modified December 21, 2016