| UofA | Computing Science | Semester 2008-1 |
Mining Health and Medical Data
(Independent Study)
Instructor: Osmar R. Zaļane
|
OBJECTIVE/DESCRIPTION:
Hospitals collect considerable amount of data about their patients,
naturally for patient-record management, archival, and possibly
decision support. There are also huge quantities of data collected by
research centers during clinical trials and controlled experiments.
Typical statistical data analysis is usually conducted on such data
for evaluation and interpretation. However, classical data retrieval
and analysis are insufficient to extract actionable hidden
patterns. Many have started applying data mining and machine learning
techniques to analyse patient records and medical data to assist in
providing better decision support and more efficient, effective and
cheaper health care.
The data collected are typically heterogeneous, noisy, multimedia
and coming from different sources with different goals, even legacy systems. Thus, data
analysts are presented with many serious challenges. Yet, techniques
such as association rule mining, clustering, outlier detecion,
supervised classification, social network analysis, time series analysis, text
mining, etc., have been used with some degree of success.
Data mining could indeed be applied on this data heterogeneous and
very large to help on three different perspectives: (1) Patient record
management to assist hospitals; (2) Decision support systems to aid
medical practitioners; and (3) Medical research to facilitate
discoveries of outbreaks, new diseases, causes or remedies.
The goal of this course is to survey the scientific literature
pertaining to the application of data mining in medical and
bio-medical data, to the application of knowledge discovery for
decision support, data management and integration, as well as the use
of data mining for the discovery and extraction of knowledge from text
and semi-structured data such as patient records for knowledge and
ontology building.
The course will mainly consist of a series of discussions on topics
relevant to data mining for health informatics leading to the
preparation of a survey paper.
Course Format:
The participants will meet once a week for one hour to one and half
hours to discuss specific research papers. Students will be asked to
give presentations on their interpretation and their personal critique
about some specific selected papers.
Each student will be assigned a particular subtopic or subtopics
related to mining health care date and will prepare a term paper.
Students will also be asked to implement some prototypes of reported
implementations and seek public datasets to test the approaches.
GRADING:
Annotated Bibliography (20%), [see web page]
Discussions (20%),
Implementation and testing (20%)
Final Term paper (40%).
TEXTBOOK and REFERENCES:
- A minimum of 10 research papers (per student) will be selected from a variety of
journals, conference proceedings and other sources.
e.g.:
- journal of Artificial Intelligence in Medicine
- Proceedings of the AMIA Annual Symposium
- Proceedings of the International Workshop on Healthcare Information and Knowledge Management
- Book: Medical Informatics: Knowledge management and atamining in Biomedicine
- ...
Distributed: December, 2007