|
|
Symbolic Methods for Biomedical Informatics
|
| General Course Information: |
BINF G4003.001 Symbolic Methods for Biomedical Informatics
TR 02:15P-03:30P
VANDERBILT C 5
F 11:00A-12:00P
VANDERBILT C 5 |
|
| Instructor Information |
Chunhua Weng
Assistant Professor
Office Address: 622 West 168th St. VC5
Telephone Number: 212-305-3317
Fax Number: 212-305-3302
E-mail: cw2384@columbia.edu
|
| TA Information: |
Jonathan Keeling
PhD Candidate, TA
Office Address: 622 West 168th St. VC5
Telephone Number: 212-342-1636
E-mail: jwk2110@columbia.edu
|
| Prerequisites |
| Second year DBMI students are preferred. However, if you have strong programming skills, solid knowledge of data structure and relational databases, and adequate familiarity with clinical information systems and electronic health records, you are welcome to contact the instructor to discuss if you can take this course in your first year. |
| Course Objectives |
| In this course, students will learn symbolic methods for processing data, information, and knowledge in biomedicine. We will go over the fundamental principles and example systems for knowledge representation, medical language processing, and data modeling. Students are expected to design and implement an independent or a collaborative (two students in maximum in each team) term-project that synthesizes these symbolic methods to support healthcare decision-making or evidence-based knowledge discovery. Students are strongly encouraged to contact the instructor to identify the relevant research resources in the department to design the project. |
| Project |
Project Examples
Jim Cimino
Create a query interface for The Medical Entities Dictionary
Jim Cimino
Create a server that uses shared memory calls (or even qrymed functions) to make the MED compatible with the Common Temrinology Services standard
David Vawdrey
Build a personalized, knowledge-based "dosing assistant" in Eclipsys
David Vawdrey
Support workflow surrounding medication ordering
Chunhua Weng
Develop an ontology-based indexing tool for The ClinicalTrials.gov
Chunhua Weng
Extract phenotypes from OMIM or any bioinformatics knowledge source
Chunhua Weng
Develop a database model for cardiac phenotype
Chunhua Weng
Create a personalized genetic test alert in Eclipsys
Many others are possible based on the interests and skill sets of the participants. Potential enrollees are encouraged to contact the instructor to clarify any questions about the course. |
| Textbook (Recommended, not Required) |
Title: Principles of Biomedical Informatics
Author: Ira Kalet
Publisher: Elsevier Science & Technology Books
Pub. Date: October 2008
ISBN-13: 9780123694386
504pp
Edition Description: New Edition |
| Academic dishonesty |
In October, 2003, the Faculty adopted the following principles of academic honesty by which students are expected to abide. These principles are the cornerstone of educational integrity. Students are expected to familiarize themselves with these principles during initial orientation and before taking an examination or submitting any work for credit toward a degree. Academic dishon¬esty — attempted or actual — will not be tolerated.
Academic dishonesty includes, but is not limited to:
1. Plagiarism: Failure to cite or otherwise acknowledge in any paper, exercise, or project submitted for credit ideas or phrases gained from another source such as published text, another person's work, or materials on the internet unless the source is obvious from the context given.
2. Self-Plagiarism: The submission of one piece of work in more than one offering or in any two exercises for credit without the explicit permission of the instructors involved.
3. Preparation by another: The submission of work as one's own that has been prepared by or purchased from another.
4. Cheating: To give, receive, take assistance, or make unau¬thorized use of information from written material, another person, his or her paper, or from any other source (except as explicitly allowed by the instructor) before or during an examination or other written exercise.
5. Violation of instructions: Failure to abide by the explicit directions or instructions of an instructor with regard to a performance for credit.
6. Falsification of work product: Falsification or misrepresen¬tation of data, evidence, or other reportable observations in any course or other exercise for credit.
7. Impermissible collaboration: The violation of the rules on acceptable collaboration on projects, papers, exercises, or examinations set by a faculty member or Law School committee.
8. Tampering with materials: Removing, hiding, or altering library materials or stealing another person's materials.
9. Facilitation of academic dishonesty: Facilitating academic dishonesty by enabling another to engage in such behavior. |
| Grading |
Grades will be a combination of class participation and a project, including
• 10% on class participation (before-class quizzes and in-class discussions and question answering)
• 30% on assignments (labs and homework)
• 20% midterm
• 5% on project proposal
• 5% on project presentation
• 30% on project paper
Participation:
Each student is expected to attend all classes, and to participate regularly and meaningfully by contributing to class discussions. If a student cannot be present to a class, he or she must notify the instructor via written documentation before class.
Assignments (Labs and Homework):
There will be about 12 lab sessions to familiarize students with related resources, including Protégé, UMLS, MED, MedLEE, and so on. 30% of grades will be based on students understanding and completion of related tasks using these research resources. Assignments will be given in lab every Friday. If the assignment is not finished in the lab period it will become homework. Assignments will be due at the beginning of the next Friday (the following lab period).
Project:
Each student will define and execute an informatics project of roughly the same scope as a (J)AMIA paper. The project topic must be approved by the instructor. Project efforts will be due on the following schedule:
Project Due Dates / Tasks
9/22 1-page project proposal including
(1) Problem definition
(2) Objectives and Significance
(3) Methods
9/24 Feedback for the topics will be returned to students
10/22 (5%) 6-9 page (single space, font 11) Project proposal due, including these components:
(1) specific aims and hypotheses (0.5-1 page)
(2) background (1-1.5 pages)
(3) research design (3-4 pages)
(4) evaluation design (2-3 pages)
12/10 (5%) Project presentations, which must include a demo of the system design/implementation and the evaluation results
12/17 (30%) 5-page final project paper in the AMIA style (template is posted under 11/6's syllabus) |
|
|
|
