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Precision psychiatry with immunological and cognitive biomarkers: a multi-domain prediction for the diagnosis of bipolar disorder or schizophrenia using machine learning

Fernandes, Brisa S., Karmakar, Chandan, Tamouza, Ryad, Tran, Truyen, Yearwood, John, Hamdani, Nora, Laouamri, Hakim, Richard, Jean-Romain, Yolken, Robert, Berk, Michael, Venkatesh, Svetha and Leboyer, Marion 2020, Precision psychiatry with immunological and cognitive biomarkers: a multi-domain prediction for the diagnosis of bipolar disorder or schizophrenia using machine learning, Translational Psychiatry, vol. 10, pp. 1-13, doi: 10.1038/s41398-020-0836-4.

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Title Precision psychiatry with immunological and cognitive biomarkers: a multi-domain prediction for the diagnosis of bipolar disorder or schizophrenia using machine learning
Author(s) Fernandes, Brisa S.ORCID iD for Fernandes, Brisa S. orcid.org/0000-0002-3797-7582
Karmakar, ChandanORCID iD for Karmakar, Chandan orcid.org/0000-0003-1814-0856
Tamouza, Ryad
Tran, TruyenORCID iD for Tran, Truyen orcid.org/0000-0001-6531-8907
Yearwood, JohnORCID iD for Yearwood, John orcid.org/0000-0002-7562-6767
Hamdani, Nora
Laouamri, Hakim
Richard, Jean-Romain
Yolken, Robert
Berk, MichaelORCID iD for Berk, Michael orcid.org/0000-0002-5554-6946
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Leboyer, Marion
Journal name Translational Psychiatry
Volume number 10
Article ID 162
Start page 1
End page 13
Total pages 13
Publisher Springer Nature
Place of publication Berlin, Germany
Publication date 2020
ISSN 2158-3188
2158-3188
Summary 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.
Language eng
DOI 10.1038/s41398-020-0836-4
Indigenous content off
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30137296

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.