Decision Support Methods for Finding Phenotype — Disorder Associations in the Bone Dysplasia Domain
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Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/532579"], "description"=>"<p>The set of dysplasias used within our experiments follow, in principle, the general characteristics of the ESDN dataset. The average maximum coverage of phenotypes is around 43%, while the average largest common set (i.e., the set of phenotypes common to all cases diagnosed with a particular disorder) is around 20%.</p>", "links"=>[], "tags"=>["skeletal", "dysplasias", "20"], "article_id"=>203072, "categories"=>["Information And Computing Sciences", "Mathematics", "Physiology", "Genetics"], "users"=>["Razan Paul", "Tudor Groza", "Jane Hunter", "Andreas Zankl"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050614.t002", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Characteristics_of_the_skeletal_dysplasias_with_more_than_20_cases_/203072", "title"=>"Characteristics of the skeletal dysplasias with more than 20 cases.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-11-30 00:51:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/532592"], "description"=>"<p>Experimental results: Accuracy per cross-validation per fold.</p>", "links"=>[], "tags"=>["cross-validation"], "article_id"=>203088, "categories"=>["Information And Computing Sciences", "Mathematics", "Physiology", "Genetics"], "users"=>["Razan Paul", "Tudor Groza", "Jane Hunter", "Andreas Zankl"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050614.t003", "stats"=>{"downloads"=>3, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Experimental_results_Accuracy_per_cross_validation_per_fold_/203088", "title"=>"Experimental results: Accuracy per cross-validation per fold.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-11-30 00:51:28"}
  • {"files"=>["https://ndownloader.figshare.com/files/532472"], "description"=>"<p>More than 70% of the bone dysplasias present in the ESDN dataset have a very small number of cases (up to 5), while those that are well represented (i.e., over 50 cases) represent a mere fraction of the total number –4%.</p>", "links"=>[], "tags"=>["dysplasia", "diagnoses", "ranges"], "article_id"=>202962, "categories"=>["Information And Computing Sciences", "Mathematics", "Physiology", "Genetics"], "users"=>["Razan Paul", "Tudor Groza", "Jane Hunter", "Andreas Zankl"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050614.g001", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Relative_distribution_of_dysplasia_diagnoses_according_to_different_ranges_of_number_of_cases_/202962", "title"=>"Relative distribution of dysplasia diagnoses according to different ranges of number of cases.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-30 00:49:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/532548"], "description"=>"<p>The table presents the top 3 and bottom 3 coverages. Coverage is computed by dividing the number of cases that contain the clinical feature by the total number of diagnoses denoting the bone dysplasias assigned to the cases. For example, <i>Cystic hygroma</i> appears in 2 of the total 4 cases diagnosed with Achondrogenesis type 1A.</p>", "links"=>[], "tags"=>["radiographic", "esdn"], "article_id"=>203046, "categories"=>["Information And Computing Sciences", "Mathematics", "Physiology", "Genetics"], "users"=>["Razan Paul", "Tudor Groza", "Jane Hunter", "Andreas Zankl"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050614.t001", "stats"=>{"downloads"=>4, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Coverage_of_clinical_and_radiographic_features_in_the_ESDN_dataset_/203046", "title"=>"Coverage of clinical and radiographic features in the ESDN dataset.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-11-30 00:50:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/285963"], "description"=>"<div><p>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.</p> </div>", "links"=>[], "tags"=>["methods", "phenotype", "associations", "dysplasia", "domain"], "article_id"=>116375, "categories"=>["Information And Computing Sciences", "Mathematics", "Physiology", "Genetics"], "users"=>["Razan Paul", "Tudor Groza", "Jane Hunter", "Andreas Zankl"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050614", "stats"=>{"downloads"=>14, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Decision_Support_Methods_for_Finding_Phenotype_Disorder_Associations_in_the_Bone_Dysplasia_Domain__/116375", "title"=>"Decision Support Methods for Finding Phenotype — Disorder Associations in the Bone Dysplasia Domain", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-11-30 01:46:15"}
  • {"files"=>["https://ndownloader.figshare.com/files/532621"], "description"=>"<p>Our solution outperforms the five Machine Learning approaches we have considered within our experiments: around 4% more accuracy than Naive Bayes and around 6% more accuracy than SVM. Although Naive Bayes has performed very well, its results are boosted by overfitting the classes that had more data (e.g., ) at the expense of others, such as for which it achieved 0 precision and recall. Unlike Naive Bayes, our approach has performed fairly uniform and consistent across all classes.</p>", "links"=>[], "tags"=>["comparative", "considered"], "article_id"=>203117, "categories"=>["Information And Computing Sciences", "Mathematics", "Physiology", "Genetics"], "users"=>["Razan Paul", "Tudor Groza", "Jane Hunter", "Andreas Zankl"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050614.t004", "stats"=>{"downloads"=>5, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Experimental_results_Overall_comparative_accuracy_across_all_considered_approaches_/203117", "title"=>"Experimental results: Overall comparative accuracy across all considered approaches.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-11-30 00:51:57"}

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Relative Metric

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