Model for personalized diagnostics and treatment in neurology—German Academy for Rare Neurological Diseases

Translated title of the contribution: Model für personalisierte Diagnostik und Therapie in der Neurologie – Deutsche Akademie für Seltene Neurologische Erkrankungen

Alexander Münchau*, Ludger Schöls, Christine Klein, Holm Graessner

*Corresponding author for this work

Abstract

The German Academy for Rare Neurological Diseases (DASNE) founded in 2017 on the Wartburg in Eisenach, aims to pave the way for an optimized personalized management of patients in all age groups with rare neurological diseases. By bringing rare neurological disease experts together and through forming a dynamic national network the DASNE, initiated by the Centers for Rare Diseases in Lübeck and Tübingen, will continuously foster mutual exchange. Members of the DASNE are renowned experts covering the whole spectrum of rare neurological disorders including pediatric neurology. Through case presentations and multidisciplinary discussion both at yearly meetings and on an internet platform, the main aims of DASNE are to establish a German expertise and reference network for rare neurological disorders. Further main aims are to provide continuous medical education for younger academics in the field of rare neurological disorders and facilitate translation.

Translated title of the contributionModel für personalisierte Diagnostik und Therapie in der Neurologie – Deutsche Akademie für Seltene Neurologische Erkrankungen
Original languageGerman
JournalNervenarzt
Volume90
Issue number8
Pages (from-to)796-803
Number of pages8
ISSN0028-2804
DOIs
Publication statusPublished - 01.08.2019

Research Areas and Centers

  • Research Area: Medical Genetics

DFG Research Classification Scheme

  • 2.23-04 Cognitive, Systems and Behavioural Neurobiology

Fingerprint

Dive into the research topics of 'Model für personalisierte Diagnostik und Therapie in der Neurologie – Deutsche Akademie für Seltene Neurologische Erkrankungen'. Together they form a unique fingerprint.

Cite this