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

Jessica J. Y. Lee, Wyeth W. Wasserman, Georg F. Hoffmann, Clara D. M. van Karnebeek, Nenad Blau

Research output: Contribution to journalArticleAcademicpeer-review

62 Citations (Scopus)


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
Original languageEnglish
Pages (from-to)151-158
JournalGenetics in medicine
Issue number1
Early online date2017
Publication statusPublished - 2018

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