Knowledge base and mini-expert platform for the diagnosis of inborn errors of metabolism
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...
Gespeichert in:
| Hauptverfasser: | , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2018
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| In: |
Genetics in medicine
Year: 2017, Jahrgang: 20, Heft: 1, Pages: 151-158 |
| ISSN: | 1530-0366 |
| DOI: | 10.1038/gim.2017.108 |
| Online-Zugang: | Verlag, Volltext: https://doi.org/10.1038/gim.2017.108 Verlag: https://www.nature.com/articles/gim2017108 
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| Verfasserangaben: | Jessica J.Y. Lee, Wyeth W. Wasserman, Georg F. Hoffmann, Clara D.M. van Karnebeek and Nenad Blau |
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| 245 | 1 | 0 | |a Knowledge base and mini-expert platform for the diagnosis of inborn errors of metabolism |c Jessica J.Y. Lee, Wyeth W. Wasserman, Georg F. Hoffmann, Clara D.M. van Karnebeek and Nenad Blau |
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| 520 | |a 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. 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. 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. 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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