Browse by author
Lookup NU author(s): Rachel ThompsonORCiD, Professor Hanns Lochmuller
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2018 The Author(s). Background: Congenital myasthenic syndromes (CMS) are a heterogeneous group of inherited neuromuscular disorders sharing the common feature of fatigable weakness due to defective neuromuscular transmission. Despite rapidly increasing knowledge about the genetic origins, specific features and potential treatments for the known CMS entities, the lack of standardized classification at the most granular level has hindered the implementation of computer-based systems for knowledge capture and reuse. Where individual clinical or genetic entities do not exist in disease coding systems, they are often invisible in clinical records and inadequately annotated in information systems, and features that apply to one disease but not another cannot be adequately differentiated. Results: We created a detailed classification of all CMS disease entities suitable for use in clinical and genetic databases and decision support systems. To avoid conflict with existing coding systems as well as with expert-defined group-level classifications, we developed a collaboration with the Orphanet nomenclature for rare diseases, creating a clinically understandable name for each entity and placing it within a logical hierarchy that paves the way towards computer-aided clinical systems and improved knowledge bases for CMS that can adequately differentiate between types and ascribe relevant expert knowledge to each. Conclusions: We suggest that data science approaches can be used effectively in the clinical domain in a way that does not disrupt preexisting expert classification and that enhances the utility of existing coding systems. Our classification provides a comprehensive view of the individual CMS entities in a manner that supports differential diagnosis and understanding of the range and heterogeneity of the disease but that also enables robust computational coding and hierarchy for machine-readability. It can be extended as required in the light of future scientific advances, but already provides the starting point for the creation of FAIR (Findable, Accessible, Interoperable and Reusable) knowledge bases of data on the congenital myasthenic syndromes.
Author(s): Thompson R, Abicht A, Beeson D, Engel AG, Eymard B, Maxime E, Lochmuller H
Publication type: Article
Publication status: Published
Journal: Orphanet Journal of Rare Diseases
Year: 2018
Volume: 13
Issue: 1
Online publication date: 26/11/2018
Acceptance date: 14/11/2018
Date deposited: 19/12/2018
ISSN (electronic): 1750-1172
Publisher: BioMed Central Ltd.
URL: https://doi.org/10.1186/s13023-018-0955-7
DOI: 10.1186/s13023-018-0955-7
PubMed id: 30477555
Altmetrics provided by Altmetric
