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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Bibliographic Details
Published in:PloS one 2012-11, Vol.7 (11), p.e50614-e50614
Main Authors: Paul, Razan, Groza, Tudor, Hunter, Jane, Zankl, Andreas
Format: Article
Language:English
Subjects:
Analysis
Artificial intelligence
Bioinformatics
Biology
Bone Diseases, Developmental - diagnosis
Bone Diseases, Developmental - genetics
Bone dysplasia
Classification
Clinical decision making
Collaboration
Computer Science
Cybernetics
Data Interpretation, Statistical
Data mining
Decision making
Decision support systems
Diagnosis
Disorders
Dysplasia
Fruits
Genetic disorders
Genotype & phenotype
Humans
International conferences
Knowledge
Knowledge bases (artificial intelligence)
Learning algorithms
Machine learning
Mathematics
Medical research
Methods
Ontology
Phenotype
Probability
Reproducibility of Results
Researchers
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0050614