College of Health and Human Services. Fall Syllabus

Transcription

1 College of Health and Human Services Fall 2013 Syllabus information placement Instructor description objectives HAP 780 : Data Mining in Health Care Time: Mondays, 7.20pm 10pm (except for 3 rd lecture that is on Wednesday) Location: Health Informatics Learning Lab (HILL) Northeast Module, Room 107 ( ) Core ( X) Concentration ( X) Elective ( ) Pre- requisite(s) ( X) (s) recommended before taking this course: HAP 700, HAP 709, HAP 602 Janusz Wojtusiak PhD jwojtusi@gmu.edu Office Hours by appointment only An introductory course to data mining and knowledge discovery in health care. Methods for mining health care databases and synthesizing task-oriented 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.

2 Required textbook(s) and/or materials 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. Required Text: Class notes and slides. Assigned Readings: Han, J., Kamber, M., Pei, J. (2011), Data Mining: Concepts and Techniques, 3 rd edition, Morgan Kaufmann. 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. requirements Computer requirements This is a computationally intensive course and you are expected to access databases, software tools, and other contents. You will need: Fast computer (multicore PC or Mac) with at least 50GB of free disk space and at least 2GM RAM. Fast internet connection Microsoft office for viewing and preparing files Other software will be provided in class If you do not have sufficient computer, you can request access to Health Informatics Learning Lab, located in Northeast Module, or use one of computer labs at GMU. Expectations: Students are responsible for assigned readings, class content and material. Students are also responsible for finding right computer equipment that allows accessing the course materials, using data and software tools, and for checking /blackboard on daily basis. Evaluation Methods: If you are taking this course as part of a graduate level course, you will receive a grade. Your grade will depend on your participation, quality of your project work and your team work. Assignments and projects are graded based on multiple criteria that will be discussed in detail. Always write all answers in own words. Do not copy-and-paste. You can ask questions by sending to the instructor. In most cases you will receive response within 48 hours.

3 Membership and Participation in a Professional Organization Class participation also means that you would become a member of a professional organization such as HIMSS, AMIA, AHIMA or other local or national organizations focused on your career. You should attend a meeting (conference, seminar, local chapter meeting, etc.) and write about a page description of what you learned and how the membership and the attended event relates to this course. It is not sufficient to simply pay the membership fee and do not participate in the organization in any way. The membership report is due last day of classes. Look for a meeting early in the semester. Final Project Data mining requires combining theoretical knowledge with practical skills. In order to develop skills in the context of health care applications, semester- long project is the most important component of the grade. The project topics should be related to analyzing healthcare data in order to solve clinical or administrative problems. The project should include, but be not limited to: (1) problem description; (2) data selection; (3) data pre- processing; (4) selection DM methods; (5) application of methods; (6) analysis of results; (7) review of available literature and related work; (7) conclusions and description of impact on healthcare. Direct application of existing software to publically available datasets is not sufficient. The projects must demonstrate significant efforts in data manipulation, processing, and mining. Projects must also illustrate understanding of applied techniques as well as healthcare problem. Teaching methods Evaluation Grading Scale ( X) Lecture ( ) Group work ( ) Independent research ( ) Field work ( )Papers ( ) Guest speakers ( ) Student presentations ( ) Case Studies ( X) Lab ( ) Class discussion ( ) Other Weekly Assignments 35% DM topic presentation 10% Participation in Professional Organization 5% Semester-long project 50% 96+ A A B B B C 0-70 F

4 Mason Honor Code Individuals with Disabilities E- mail Policy The complete Honor Code is as follows: 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. The university is committed to providing equal access to employment and educational opportunities for people with disabilities. Mason recognizes that individuals with disabilities may need reasonable accommodations to have equally effective opportunities to participate in or benefit from the university educational programs, services, and activities, and have equal employment opportunities. The university will adhere to all applicable federal and state laws, regulations, and guidelines with respect to providing reasonable accommodations as necessary to afford equal employment opportunity and equal access to programs for qualified people with disabilities. Applicants for admission and students requesting reasonable accommodations for a disability should call the Office of Disability Services at Employees and applicants for employment should call the Office of Equity and Diversity Services at Questions regarding reasonable accommodations and discrimination on the basis of disability should be directed to the Americans with Disabilities Act (ADA) coordinator in the Office of Equity and Diversity Services. Web: masonlive.gmu.edu Mason uses electronic mail to provide official information to students. Examples include notices from the library, notices about academic standing, financial aid information, class materials, assignments, questions, and instructor feedback. Students are responsible for the content of university communication sent to their Mason e- mail account and are required to activate that account and check it regularly. Students are also expected to maintain an active and accurate mailing address in order to receive communications sent through the United States Postal Service.

5 Tentative Weekly Schedule The schedule below is approximate and may be changed to adapt to students' needs and requests, new material, and for other reasons. Due dates for all assignments are subject to change and will be provided with assignments. Wk Date Topics Assignments Due Date 1 8/26 Introduction to data mining in health care What do you know? 9/1 Review of databases Introduction to software 9/2 No Class Labor Day 2 9/9 Measuring/Describing the world 9/15 Data Preprocessing - part 1 prepare sample data 3 WED. Data preprocessing - part 2 9/22 9/18 Knowledge representation prepare sample data 4 9/23 Exploratory data analysis 9/29 Statistics Review of types of health data 5 9/30 Mining Frequent Patterns/Associations 10/6 6 10/7 Classification and Regression: Basics 10/ /14 Classification 10/ /21 Cluster Analysis 10/ /28 Outlier Detection 11/ /4 Time and Space 11/ /11 Text and Image Mining 11/ /18 BIG DATA Analysis 11/ /25 Review of Data Mining Applications in 12/1 Health Care and Research Frontiers 14 12/2 Final Project Presentations All Missing Assignments Due