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. Attached Files Name Description MIMEType Size Downloads 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.org/0000-0002-3797-7582 Karmakar, Chandan orcid.org/0000-0003-1814-0856 Tamouza, Ryad Tran, Truyen orcid.org/0000-0001-6531-8907 Yearwood, John orcid.org/0000-0002-7562-6767 Hamdani, Nora Laouamri, Hakim Richard, Jean-Romain Yolken, Robert Berk, Michael orcid.org/0000-0002-5554-6946 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 Document type: Journal Article Collections: Faculty of Health School of Medicine Faculty of Science, Engineering and Built Environment School of Information Technology Open Access Collection A2I2 (Applied Artificial Intelligence Institute) Related Links Link Description Link to full-text (open access)     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. 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. Versions Version Filter Type Wed, 27 May 2020, 09:48:19 EST Thu, 28 May 2020, 05:00:13 EST Thu, 28 May 2020, 06:03:48 EST Thu, 28 May 2020, 12:08:21 EST Thu, 28 May 2020, 12:11:29 EST Thu, 28 May 2020, 12:14:16 EST Sat, 13 Jun 2020, 02:31:24 EST Sat, 13 Jun 2020, 02:33:29 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 69 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Wed, 27 May 2020, 09:48:19 EST

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.