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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
medicine
Precision psychiatry with immunological and cognitive biomarkers: a multi-domain prediction for the diagnosis of bipolar disorder or schizophrenia using machine learning
Translational Psychiatry, Volume 10, No. 1, Article 162, Year 2020
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Description
Precision psychiatry is attracting increasing attention lately as a recognized priority. One of the goals of precision psychiatry is to develop tools capable of aiding a clinically informed psychiatric diagnosis objectively. Cognitive, inflammatory and immunological factors are altered in both bipolar disorder (BD) and schizophrenia (SZ), however, most of these alterations do not respect diagnostic boundaries from a phenomenological perspective and possess great variability in different individuals with the same phenotypic diagnosis and, consequently, none so far has proven to have the ability of reliably aiding in the differential diagnosis of BD and SZ. We developed a probabilistic multi-domain data integration model consisting of immune and inflammatory biomarkers in peripheral blood and cognitive biomarkers using machine learning to predict diagnosis of BD and SZ. A total of 416 participants, being 323, 372, and 279 subjects for blood, cognition and combined biomarkers analysis, respectively. Our multi-domain model performances for the BD vs. control (sensitivity 80% and specificity 71%) and for the SZ vs. control (sensitivity 84% and specificity 81%) pairs were high in general, however, our multi-domain model had only moderate performance for the differential diagnosis of BD and SZ (sensitivity 71% and specificity 73%). In conclusion, our results show that the diagnosis of BD and of SZ, and that the differential diagnosis of BD and SZ can be predicted with possible clinical utility by a computational machine learning algorithm employing blood and cognitive biomarkers, and that their integration in a multi-domain outperforms algorithms based in only one domain. Independent studies are needed to validate these findings. © 2020, The Author(s).
Authors & Co-Authors
Tamouza, Ryad
France, Paris
Ap-hp Assistance Publique - Hopitaux de Paris
France, Creteil
Fondamental Foundation
Hamdani, Nora
France, Paris
Ap-hp Assistance Publique - Hopitaux de Paris
Yolken, Robert H.
United States, Baltimore
Johns Hopkins School of Medicine
Berk, Michael
Australia, Geelong
Barwon Health
Australia, Melbourne
Orygen Youth Health
Leboyer, Marion
France, Paris
Ap-hp Assistance Publique - Hopitaux de Paris
France, Creteil
Fondamental Foundation
Statistics
Citations: 24
Authors: 5
Affiliations: 7
Identifiers
Doi:
10.1038/s41398-020-0836-4
ISSN:
21583188
Research Areas
Mental Health
Study Design
Phenomenological Study
Study Approach
Qualitative