Decision support methods for finding phenotype - disorder associations in the bone dysplasia domain

Razan, Paul, Groza, Tudor, Hunter, Jane and Zankl, Andreas (2012) Decision support methods for finding phenotype - disorder associations in the bone dysplasia domain. PLoS One, 7 11: e50614.1-e50614.10. doi:10.1371/journal.pone.0050614


Author Razan, Paul
Groza, Tudor
Hunter, Jane
Zankl, Andreas
Total Author Count Override 4
Title Decision support methods for finding phenotype - disorder associations in the bone dysplasia domain
Journal name PLoS One   Check publisher's open access policy
ISSN 1932-6203
Publication date 2012-11-30
Sub-type Article (original research)
DOI 10.1371/journal.pone.0050614
Open Access Status DOI
Volume 7
Issue 11
Start page e50614.1
End page e50614.10
Total pages 10
Place of publication San Francisco, CA, United States
Publisher Public Library of Science
Collection year 2013
Language eng
Abstract 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.
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Article number e50614

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2013 Collection
School of Information Technology and Electrical Engineering Publications
School of Medicine Publications
 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 4 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 4 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Thu, 06 Dec 2012, 16:16:20 EST by Dr Tudor Groza on behalf of School of Information Technol and Elec Engineering