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Knowledge base and mini-expert platform for the diagnosis of inborn errors of metabolism

PURPOSE: Recognizing individuals with inherited diseases can be difficult because signs and symptoms often overlap those of common medical conditions. Focusing on inborn errors of metabolism (IEMs), we present a method that brings the knowledge of highly specialized experts to professionals involved...

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Autores principales: Lee, Jessica J Y, Wasserman, Wyeth W, Hoffmann, Georg F, van Karnebeek, Clara D M, Blau, Nenad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5763153/
https://www.ncbi.nlm.nih.gov/pubmed/28726811
http://dx.doi.org/10.1038/gim.2017.108
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author Lee, Jessica J Y
Wasserman, Wyeth W
Hoffmann, Georg F
van Karnebeek, Clara D M
Blau, Nenad
author_facet Lee, Jessica J Y
Wasserman, Wyeth W
Hoffmann, Georg F
van Karnebeek, Clara D M
Blau, Nenad
author_sort Lee, Jessica J Y
collection PubMed
description PURPOSE: Recognizing individuals with inherited diseases can be difficult because signs and symptoms often overlap those of common medical conditions. Focusing on inborn errors of metabolism (IEMs), we present a method that brings the knowledge of highly specialized experts to professionals involved in early diagnoses. We introduce IEMbase, an online expert-curated IEM knowledge base combined with a prototype diagnosis support (mini-expert) system. METHODS: Disease-characterizing profiles of specific biochemical markers and clinical symptoms were extracted from an expert-compiled IEM database. A mini-expert system algorithm was developed using cosine similarity and semantic similarity. The system was evaluated using 190 retrospective cases with established diagnoses, collected from 15 different metabolic centers. RESULTS: IEMbase provides 530 well-defined IEM profiles and matches a user-provided phenotypic profile to a list of candidate diagnoses/genes. The mini-expert system matched 62% of the retrospective cases to the exact diagnosis and 86% of the cases to a correct diagnosis within the top five candidates. The use of biochemical features in IEM annotations resulted in 41% more exact phenotype matches than clinical features alone. CONCLUSION: IEMbase offers a central IEM knowledge repository for many genetic diagnostic centers and clinical communities seeking support in the diagnosis of IEMs.
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spelling pubmed-57631532018-01-12 Knowledge base and mini-expert platform for the diagnosis of inborn errors of metabolism Lee, Jessica J Y Wasserman, Wyeth W Hoffmann, Georg F van Karnebeek, Clara D M Blau, Nenad Genet Med Original Research Article PURPOSE: Recognizing individuals with inherited diseases can be difficult because signs and symptoms often overlap those of common medical conditions. Focusing on inborn errors of metabolism (IEMs), we present a method that brings the knowledge of highly specialized experts to professionals involved in early diagnoses. We introduce IEMbase, an online expert-curated IEM knowledge base combined with a prototype diagnosis support (mini-expert) system. METHODS: Disease-characterizing profiles of specific biochemical markers and clinical symptoms were extracted from an expert-compiled IEM database. A mini-expert system algorithm was developed using cosine similarity and semantic similarity. The system was evaluated using 190 retrospective cases with established diagnoses, collected from 15 different metabolic centers. RESULTS: IEMbase provides 530 well-defined IEM profiles and matches a user-provided phenotypic profile to a list of candidate diagnoses/genes. The mini-expert system matched 62% of the retrospective cases to the exact diagnosis and 86% of the cases to a correct diagnosis within the top five candidates. The use of biochemical features in IEM annotations resulted in 41% more exact phenotype matches than clinical features alone. CONCLUSION: IEMbase offers a central IEM knowledge repository for many genetic diagnostic centers and clinical communities seeking support in the diagnosis of IEMs. Nature Publishing Group 2018-01 2017-07-20 /pmc/articles/PMC5763153/ /pubmed/28726811 http://dx.doi.org/10.1038/gim.2017.108 Text en Copyright © 2018 American College of Medical Genetics and Genomics http://creativecommons.org/licenses/by-nc-nd/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/
spellingShingle Original Research Article
Lee, Jessica J Y
Wasserman, Wyeth W
Hoffmann, Georg F
van Karnebeek, Clara D M
Blau, Nenad
Knowledge base and mini-expert platform for the diagnosis of inborn errors of metabolism
title Knowledge base and mini-expert platform for the diagnosis of inborn errors of metabolism
title_full Knowledge base and mini-expert platform for the diagnosis of inborn errors of metabolism
title_fullStr Knowledge base and mini-expert platform for the diagnosis of inborn errors of metabolism
title_full_unstemmed Knowledge base and mini-expert platform for the diagnosis of inborn errors of metabolism
title_short Knowledge base and mini-expert platform for the diagnosis of inborn errors of metabolism
title_sort knowledge base and mini-expert platform for the diagnosis of inborn errors of metabolism
topic Original Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5763153/
https://www.ncbi.nlm.nih.gov/pubmed/28726811
http://dx.doi.org/10.1038/gim.2017.108
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