George Mason University – Graduate Council
Approval Form
Graduate Course
All courses numbered 500 or above must be submitted to the Graduate Council for final approval
after approval by the sponsoring College, School or Institute.
Graduate Council requires submission of this form for a new course or any change to existing
courses. For a new course, please attach a copy of the syllabus and catalog description (with
catalog credit format, e.g. 3:2:1). The designated representative of the College, School or
Institute should forward the form along with the syllabus and catalog description, if required, as
an email attachment (in one file) to the secretary of the Graduate Council. A printed copy of the
form with signatures and the attachments should be brought to the Graduate Council meeting.
Please complete the Graduate Course Coordinator Form if the proposed changes will affect other
units.
Note: Colleges, Schools or Institutes are responsible for submitting new or modified catalog
descriptions (35 words or less, using catalog format) to Creative Services by deadlines outlined in
the yearly Catalog production calendar.
Please indicate: New___X____
Modify_______
Delete_______
Department/Unit:__Health Administration and Policy/CHHS_
Course Subject/Number:___HAP 780________
Submitted by:__Yiota Kitsantas__ Ext:_3-1964__ Email:_pkitsant@gmu.edu____
Course Title:_Data Mining in Health Care_________
Effective Term (New/Modified Courses only):_fall 2008 Final Term (deleted courses only):_____
Credit Hours: (Fixed) __3__
(Var.) ______
___X__ Regular graduate (A, B, C, etc.)
to ______
Grade Type (check one):
_____
Satisfactory/No Credit only
_____
Special graduate (A, B, C, etc. + IP)
Repeat Status*(check one): ___ NR-Not repeatable ____ RD-Repeatable within degree
____ RT-Repeatable within term
*Note: Used only for special topics, independent study, or internships courses
Total Number of
Hours Allowed: _6______
Schedule Type Code(s): 1._LEC_ LEC=Lecture SEM=Seminar STU=Studio
INT=Internship IND=Independent Study
2.____
LAB=Lab RCT=Recitation (second code used only for courses with Lab or Rct component)
Prereq __ Coreq ___ (Check 0ne) Healthcare Financial Management or Equivalent or Instructor’s Permission
________________________________________________________________________
__________________
Note: Modified courses - review prereq or coreq for necessary changes; Deleted courses - review other courses to correct
prereqs that list the deleted course.
Description of Modification (for modified
courses):____________________________________________________________________
Special Instructions (major/college/class code restrictions, if
needed):__________________________________________
Department/Unit Approval Signature:_________________________________________
Date: _____________
College/School Committee Approval Signature:__________________________________
Date:_____________
Graduate Council Approval Date:____________ Provost Office
Signature:_________________________________
George Mason University
Coordination Form
Graduate Course
Approval from other units:
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Unit:
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Graduate Council approval: ______________________________________________
Date: ____________
Graduate Council representative: __________________________________________
Date: ____________
Provost Office representative: ____________________________________________
Date: ____________
GEORGE MASON UNIVERSITY
College of Health and Human Services
Department of Health Administration and Policy
Course Number: HAP 780 (3 credits)
Course Title:
Data Mining in Health Care
Prerequisite: HAP 501 or equivalent introductory statistics course and lab, introductory
database course; or permission of the instructor.
Course Description: This is an introductory course to data mining and knowledge
discovery in health care. Methods for mining health care databases and synthesizing taskoriented knowledge from computer data and prior knowledge are emphasized. Topics
include fundamental concepts of data mining, data preprocessing, classification and
prediction (decision trees, attributional rules, Bayesian networks) constructive induction,
cluster and association analysis, knowledge representation and visualization, and an
overview of practical tools for discovering knowledge from medical data. These topics
are illustrated by examples of practical applications in health care.
Upon completion of the course, students will be able to:
1. Understand and describe data mining techniques and their use in knowledge discovery
as it applies to health related fields.
2. Define a health related problem to be solved by means of data mining.
3. Apply data preprocessing techniques to clean and prepare data sets for analysis.
4. Built and assess predictive models using various techniques such as decision trees,
decision rules, Bayesian networks and clustering.
5. Develop skills of using recent data mining software for solving practical problems in
health services research and other medical and public health related fields.
6. Use methods for presenting knowledge in natural language and other understandable
forms.
7. Review and critique current research papers on data mining algorithms and
implementations.
Tentative Schedule
Topics
Week 1
Introduction to Data Mining in Health Care
- Motivation and goals
- Fundamental concepts
Introduction to software tools
Week 2
Measuring/Describing the world
Data Preprocessing I
Week 3
Data preprocessing II
Knowledge representation
Week 4
Exploratory Data Analysis
Statistical data analysis
Week 5
Cluster Analysis
Week 6
Decision & regression trees
Week 7
Bayesian Learning I
Week 8
Bayesian Learning II
Week 9
AQ learning, Decision rules
Week 10
Constructive Induction
Visualization
Natural language generation
Week 11
Association rules
Week 12
Integrated approaches: Inductive databases
Week 13
Review of applications
Summary and future directions
Week 14
Presentations of students’ projects
Teaching Strategies:
Lecture, discussion, computer lab for demonstrating the use of software in data mining,
and group work.
Required Text:
Class notes and slides
Assigned Readings:
Bishop C.M. (2007). Pattern Recognition and Machine Learning. Springer.
Black K. (2008). Business Statistics for Contemporary Decision Making. New Jersey:
John Wiley & Sons.
Witten I.H., Frank E. (2005). Data Mining: Practical Machine Learning Tools and
Techniques, second edition. Morgan Kaufmann.
Evaluation:
Students will be evaluated based on exams, homework problems, presentation and
projects involving data mining.
Policies and Guidelines
Honor code:
“To promote a stronger sense of mutual responsibility, respect, trust, and fairness among
all members of the George Mason University community and with the desire for greater
academic and personal achievement, we, the student members of the university
community, have set forth this honor code: Student members of the George Mason
University community pledge not to cheat, plagiarize, steal, or lie in matters related to
academic work” (George Mason University Catalog, 2006-2007, p. 31).
Individuals with disabilities:
George Mason University is committed to complying with the Rehabilitation Act of 1973
and the Americans with Disabilities Act of 1990 by providing reasonable
accommodations for disabled applicants for admission, students, applicants for
employment, employees, and visitors. Applicants for admission and students requiring
specific accommodations for a disability should contact the Disability Resource Center at
703-993-2474, or the Equity Office at 703-993-8730. Applicants for employment and
employees should contact Human Resources at 703-993-2600 or the Equity Office.
Students and employees are responsible for providing appropriate documentation and
requesting reasonable accommodation in a timely manner (George Mason University
Catalog, 200x-200x, p. xx).
Memorandum defining policies and guidelines for students / faculty course
requirements:
The student is responsible for completing all course requirements on time unless
expressed permission for late submission has been granted in advance by the
instructor, except for cases of illness or death in the family.
If a student fails to submit assignments or take exams as scheduled, it is the students'
responsibility to contact the professor in advance of the missed deadline. For every day
beyond the deadline, 5 points will be deducted from the grade for that assignment or
exam.
For final examinations the catalog policy shall prevail: Absence from final examinations
will not be excused except for sickness on the day of the examination or for other caused
approved by the student's academic dean (George Mason University Catalog, 2006-2007,
p. 35).
E-Mail accounts:
Students will be expected to use their GMU e-mail account. The student must be able to
receive emails and other communication as specified. Do not allow the mailbox to
become full.