Help Privacy Policy Disclaimer
  Advanced SearchBrowse





Subtyping psychiatric disorders using unsupervised learning methods


Pelin,  Helena
RG Statistical Genetics, Max Planck Institute of Psychiatry, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)

Pelin Helena.pdf
(Any fulltext), 3MB

Supplementary Material (public)
There is no public supplementary material available

Pelin, H. (2022). Subtyping psychiatric disorders using unsupervised learning methods. PhD Thesis, Technische Universität München, München.

Cite as: https://hdl.handle.net/21.11116/0000-000B-4245-D
Psychiatric disorders are highly heterogeneous regarding their symptoms and disease course. The aim of this thesis was to apply computational methods to generate new knowledge in the field of classification of psychiatric diseases and new subtypes discovery. Machine learning methods were used to identify and characterize the clusters in a transdiagnostic sample consisting of healthy controls and psychiatric disorders.
Übersetzte Kurzfassung:
Psychiatrische Erkrankungen sind in Bezug auf Symptome und Krankheitsverlauf sehr heterogen. Ziel dieser Arbeit war es, computergestützte Methoden anzuwenden, um neue Erkenntnisse auf dem Gebiet der Klassifikation psychiatrischer Erkrankungen zu generieren. Maschinelles Lernen wurde verwendet, um die Cluster in einer transdiagnostischen Stichprobe bestehend aus gesunden Kontrollpersonen und psychiatrischen Störungen zu identifizieren und zu charakterisieren.