Deconstructing depression by machine learning: the POKAL-PSY study.
Biological psychiatry
MDD
Machine learning
Phenotyping
Journal
European archives of psychiatry and clinical neuroscience
ISSN: 1433-8491
Titre abrégé: Eur Arch Psychiatry Clin Neurosci
Pays: Germany
ID NLM: 9103030
Informations de publication
Date de publication:
13 Dec 2023
13 Dec 2023
Historique:
received:
26
04
2023
accepted:
04
11
2023
medline:
13
12
2023
pubmed:
13
12
2023
entrez:
13
12
2023
Statut:
aheadofprint
Résumé
Unipolar depression is a prevalent and disabling condition, often left untreated. In the outpatient setting, general practitioners fail to recognize depression in about 50% of cases mainly due to somatic comorbidities. Given the significant economic, social, and interpersonal impact of depression and its increasing prevalence, there is a need to improve its diagnosis and treatment in outpatient care. Various efforts have been made to isolate individual biological markers for depression to streamline diagnostic and therapeutic approaches. However, the intricate and dynamic interplay between neuroinflammation, metabolic abnormalities, and relevant neurobiological correlates of depression is not yet fully understood. To address this issue, we propose a naturalistic prospective study involving outpatients with unipolar depression, individuals without depression or comorbidities, and healthy controls. In addition to clinical assessments, cardiovascular parameters, metabolic factors, and inflammatory parameters are collected. For analysis we will use conventional statistics as well as machine learning algorithms. We aim to detect relevant participant subgroups by data-driven cluster algorithms and their impact on the subjects' long-term prognosis. The POKAL-PSY study is a subproject of the research network POKAL (Predictors and Clinical Outcomes in Depressive Disorders; GRK 2621).
Identifiants
pubmed: 38091084
doi: 10.1007/s00406-023-01720-9
pii: 10.1007/s00406-023-01720-9
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Deutsche Forschungsgemeinschaft
ID : GRK 2621
Organisme : Deutsche Forschungsgemeinschaft
ID : GRK 2621
Organisme : Deutsche Forschungsgemeinschaft
ID : GRK 2621
Investigateurs
Tobias Dreischulte
(T)
Peter Henningsen
(P)
Markus Bühner
(M)
Katharina Biersack
(K)
Constantin Brand
(C)
Vita Brisnik
(V)
Christopher Ebert
(C)
Feyza Gökce
(F)
Carolin Haas
(C)
Lukas Kaupe
(L)
Jonas Raub
(J)
Philipp Reindl-Spanner
(P)
Hannah Schillock
(H)
Petra Schönweger
(P)
Victoria von Schrottenberg
(V)
Jochen Vukas
(J)
Puya Younesi
(P)
Caroline Jung-Sievers
(C)
Helmut Krcmar
(H)
Karoline Lukaschek
(K)
Kirsten Lochbühler
(K)
Gabriele Pitschel-Walz
(G)
Informations de copyright
© 2023. The Author(s).
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