BioSpace Collaborative

Academic/Biomedical Research
News & Jobs
Biotechnology and Pharmaceutical Channel Medical Device and Diagnostics Channel Clinical Research Channel BioSpace Collaborative    Job Seekers:  Register | Login          Employers:  Register | Login  

NEWSLETTERS
Free Newsletters
Archive
My Subscriptions

NEWS
News by Subject
News by Disease
News by Date
PLoS
Search News
Post Your News
JoVE

CAREER NETWORK
Job Seeker Login
Most Recent Jobs
Search Jobs
Post Resume
Career Fairs
Career Resources
For Employers

HOTBEDS
Regional News
US & Canada
  Biotech Bay
  Biotech Beach
  Genetown
  Pharm Country
  BioCapital
  BioMidwest
  Bio NC
  BioForest
  Southern Pharm
  BioCanada East
  C2C Services & Suppliers™
Europe
Asia

DIVERSITY

PROFILES
Company Profiles

INTELLIGENCE
Research Store

INDUSTRY EVENTS
Research Events
Post an Event
RESOURCES
Real Estate
Business Opportunities

PLoS By Category | Recent PLoS Articles
Computer Science - Mathematics - Physiology

Decision Support Methods for Finding Phenotype — Disorder Associations in the Bone Dysplasia Domain
Published: Friday, November 30, 2012
Author: Razan Paul et al.

by Razan Paul, Tudor Groza, Jane Hunter, Andreas Zankl

A lack of mature domain knowledge and well established guidelines makes the medical diagnosis of skeletal dysplasias (a group of rare genetic disorders) a very complex process. Machine learning techniques can facilitate objective interpretation of medical observations for the purposes of decision support. However, building decision support models using such techniques is highly problematic in the context of rare genetic disorders, because it depends on access to mature domain knowledge. This paper describes an approach for developing a decision support model in medical domains that are underpinned by relatively sparse knowledge bases. We propose a solution that combines association rule mining with the Dempster-Shafer theory (DST) to compute probabilistic associations between sets of clinical features and disorders, which can then serve as support for medical decision making (e.g., diagnosis). We show, via experimental results, that our approach is able to provide meaningful outcomes even on small datasets with sparse distributions, in addition to outperforming other Machine Learning techniques and behaving slightly better than an initial diagnosis by a clinician.
  More...