NDLI: Mining for Implications in Medical Data

Content Provider	IEEE Xplore Digital Library
Author	Bethel, C.L. Hall, L.O. Goldgof, D.
Copyright Year	2006
Description	Author affiliation: Dept. of Comput. Sci. & Eng., South Florida Univ., Tampa, FL (Bethel, C.L.; Hall, L.O.; Goldgof, D.)
Abstract	Accruing patients for clinical trials has been a tedious and time consuming task for clinicians. It requires extensive knowledge of the specific criteria for all available clinical trials. Through interviews with clinicians, implications were discovered which reduced the number of required questions/answers to determine eligibility. After gathering and recording data on past breast cancer patients, the answers to the questions asked by an expert system were extracted. An association rule learner, was used to generate implication rules such as: male => not pregnant. It was determined that all current implication rules could be recovered with 100% confidence. Further searching for additional rules resulted in the discovery of several which provided an improvement in the clinical ease of use of the Web-based clinical trial assignment expert system
Sponsorship	IEEE CPS
Starting Page	1212
Ending Page	1215
File Size	240440
Page Count	4
File Format	PDF
ISBN	0769525210
ISSN	10514651
DOI	10.1109/ICPR.2006.800
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clinical trials Breast cancer Expert systems Diseases Data mining Association rules Pregnancy Medical treatment Protocols Medical diagnostic imaging
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Clinical deployment of a medical expert system to increase accruals for clinical trials: Challenges

Parametric data mining and diagnostic rules for digital thermographs in breast cancer

A decision support system for breast cancer treatment based on data mining technologies and clinical practice guidelines

Discovery of rules for medical expert systems-rough set approach

Identifying top-k Vital Patterns from multi-class medical data

Mining Classification Rules of Cancer Patients for Traditional Chinese Medical Treatments: A Rough Set Based Approach

Mining constrained association rules to predict heart disease

Extraction of medical knowledge from clinical data

Challenging issues in iterative intelligent medical search

Mining for Implications in Medical Data

Similar Documents

Clinical deployment of a medical expert system to increase accruals for clinical trials: Challenges

Parametric data mining and diagnostic rules for digital thermographs in breast cancer

A decision support system for breast cancer treatment based on data mining technologies and clinical practice guidelines

Discovery of rules for medical expert systems-rough set approach

Identifying top-k Vital Patterns from multi-class medical data

Mining Classification Rules of Cancer Patients for Traditional Chinese Medical Treatments: A Rough Set Based Approach

Mining constrained association rules to predict heart disease

Extraction of medical knowledge from clinical data

Challenging issues in iterative intelligent medical search

Mining for Implications in Medical Data