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

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 su...

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Autores principales:	Razan Paul, Tudor Groza, Jane Hunter, Andreas Zankl
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1305f35c30814b198834ccdd38bd80c3
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spelling	oai:doaj.org-article:1305f35c30814b198834ccdd38bd80c32021-11-18T08:06:47ZDecision support methods for finding phenotype--disorder associations in the bone dysplasia domain.1932-620310.1371/journal.pone.0050614https://doaj.org/article/1305f35c30814b198834ccdd38bd80c32012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23226331/?tool=EBIhttps://doaj.org/toc/1932-6203A 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.Razan PaulTudor GrozaJane HunterAndreas ZanklPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 11, p e50614 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Razan Paul Tudor Groza Jane Hunter Andreas Zankl Decision support methods for finding phenotype--disorder associations in the bone dysplasia domain.
description	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.
format	article
author	Razan Paul Tudor Groza Jane Hunter Andreas Zankl
author_facet	Razan Paul Tudor Groza Jane Hunter Andreas Zankl
author_sort	Razan Paul
title	Decision support methods for finding phenotype--disorder associations in the bone dysplasia domain.
title_short	Decision support methods for finding phenotype--disorder associations in the bone dysplasia domain.
title_full	Decision support methods for finding phenotype--disorder associations in the bone dysplasia domain.
title_fullStr	Decision support methods for finding phenotype--disorder associations in the bone dysplasia domain.
title_full_unstemmed	Decision support methods for finding phenotype--disorder associations in the bone dysplasia domain.
title_sort	decision support methods for finding phenotype--disorder associations in the bone dysplasia domain.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/1305f35c30814b198834ccdd38bd80c3
work_keys_str_mv	AT razanpaul decisionsupportmethodsforfindingphenotypedisorderassociationsinthebonedysplasiadomain AT tudorgroza decisionsupportmethodsforfindingphenotypedisorderassociationsinthebonedysplasiadomain AT janehunter decisionsupportmethodsforfindingphenotypedisorderassociationsinthebonedysplasiadomain AT andreaszankl decisionsupportmethodsforfindingphenotypedisorderassociationsinthebonedysplasiadomain
_version_	1718422211929309184

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

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