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  1. Article ; Online: Ten Simple Rules for Using Machine Learning in Mental Health Research.

    Radua, Joaquim / Koutsouleris, Nikolaos

    Biological psychiatry

    2023  

    Language English
    Publishing date 2023-11-18
    Publishing country United States
    Document type Journal Article
    ZDB-ID 209434-4
    ISSN 1873-2402 ; 0006-3223
    ISSN (online) 1873-2402
    ISSN 0006-3223
    DOI 10.1016/j.biopsych.2023.11.012
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  2. Article ; Online: Toward clinically useful models for individualised prognostication in psychosis.

    Koutsouleris, Nikolaos

    The Lancet. Digital health

    2019  Volume 1, Issue 6, Page(s) e244–e245

    MeSH term(s) Causality ; Humans ; Machine Learning ; Psychotic Disorders/diagnosis ; Quality of Life
    Language English
    Publishing date 2019-09-12
    Publishing country England
    Document type Journal Article ; Comment
    ISSN 2589-7500
    ISSN (online) 2589-7500
    DOI 10.1016/S2589-7500(19)30122-0
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  3. Article ; Online: Annual Research Review: Translational machine learning for child and adolescent psychiatry.

    Dwyer, Dominic / Koutsouleris, Nikolaos

    Journal of child psychology and psychiatry, and allied disciplines

    2022  Volume 63, Issue 4, Page(s) 421–443

    Abstract: Children and adolescents could benefit from the use of predictive tools that facilitate personalized diagnoses, prognoses, and treatment selection. Such tools have not yet been deployed using traditional statistical methods, potentially due to the ... ...

    Abstract Children and adolescents could benefit from the use of predictive tools that facilitate personalized diagnoses, prognoses, and treatment selection. Such tools have not yet been deployed using traditional statistical methods, potentially due to the limitations of the paradigm and the need to leverage large amounts of digital data. This review will suggest that a machine learning approach could address these challenges and is designed to introduce new readers to the background, methods, and results in the field. A rationale is first introduced followed by an outline of fundamental elements of machine learning approaches. To provide an overview of the use of the techniques in child and adolescent literature, a scoping review of broad trends is then presented. Selected studies are also highlighted in order to draw attention to research areas that are closest to translation and studies that exhibit a high degree of experimental innovation. Limitations to the research, and machine learning approaches generally, are outlined in the penultimate section highlighting issues related to sample sizes, validation, clinical utility, and ethical challenges. Finally, future directions are discussed that could enhance the possibility of clinical implementation and address specific questions relevant to the child and adolescent psychiatry. The review gives a broad overview of the machine learning paradigm in order to highlight the benefits of a shift in perspective towards practically oriented statistical solutions that aim to improve clinical care of children and adolescents.
    MeSH term(s) Adolescent ; Adolescent Psychiatry ; Child ; Family ; Humans ; Machine Learning
    Language English
    Publishing date 2022-01-17
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Review
    ZDB-ID 218136-8
    ISSN 1469-7610 ; 0021-9630 ; 0373-8086
    ISSN (online) 1469-7610
    ISSN 0021-9630 ; 0373-8086
    DOI 10.1111/jcpp.13545
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  4. Article ; Online: Psychotic disorders as a framework for precision psychiatry.

    Coutts, Fiona / Koutsouleris, Nikolaos / McGuire, Philip

    Nature reviews. Neurology

    2023  Volume 19, Issue 4, Page(s) 221–234

    Abstract: People with psychotic disorders can show marked interindividual variations in the onset of illness, responses to treatment and relapse, but they receive broadly similar clinical care. Precision psychiatry is an approach that aims to stratify people with ... ...

    Abstract People with psychotic disorders can show marked interindividual variations in the onset of illness, responses to treatment and relapse, but they receive broadly similar clinical care. Precision psychiatry is an approach that aims to stratify people with a given disorder according to different clinical outcomes and tailor treatment to their individual needs. At present, interindividual differences in outcomes of psychotic disorders are difficult to predict on the basis of clinical assessment alone. Therefore, current research in psychosis seeks to build models that predict outcomes by integrating clinical information with a range of biological measures. Here, we review recent progress in the application of precision psychiatry to psychotic disorders and consider the challenges associated with implementing this approach in clinical practice.
    MeSH term(s) Humans ; Psychotic Disorders/diagnosis ; Psychotic Disorders/therapy ; Psychiatry
    Language English
    Publishing date 2023-03-06
    Publishing country England
    Document type Journal Article ; Review
    ZDB-ID 2491514-2
    ISSN 1759-4766 ; 1759-4758
    ISSN (online) 1759-4766
    ISSN 1759-4758
    DOI 10.1038/s41582-023-00779-1
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  5. Book ; Online ; Thesis: Phenotypical exploration and predictive utility of formal thought disorder in individuals with recent-onset of psychosis

    Öztürk, Ömer Faruk [Verfasser] / Koutsouleris, Nikolaos [Akademischer Betreuer]

    2024  

    Author's details Ömer Faruk Öztürk ; Betreuer: Nikolaos Koutsouleris
    Keywords Medizin, Gesundheit ; Medicine, Health
    Subject code sg610
    Language English
    Publisher Universitätsbibliothek der Ludwig-Maximilians-Universität
    Publishing place München
    Document type Book ; Online ; Thesis
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  6. Article ; Online: Promises and Pitfalls of the New Era of Computational Behavioral Neuroscience.

    Schmidt, Mathias V / Koutsouleris, Nikolaos

    Biological psychiatry

    2021  Volume 89, Issue 9, Page(s) 845–846

    MeSH term(s) Computer Simulation ; Neurosciences ; Social Defeat
    Language English
    Publishing date 2021-04-15
    Publishing country United States
    Document type Journal Article ; Comment
    ZDB-ID 209434-4
    ISSN 1873-2402 ; 0006-3223
    ISSN (online) 1873-2402
    ISSN 0006-3223
    DOI 10.1016/j.biopsych.2021.02.965
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  7. Article ; Online: The potential of precision psychiatry: what is in reach?

    Kambeitz-Ilankovic, Lana / Koutsouleris, Nikolaos / Upthegrove, Rachel

    The British journal of psychiatry : the journal of mental science

    2022  Volume 220, Issue 4, Page(s) 175–178

    Abstract: Progress in developing personalised care for mental disorders is supported by numerous proof-of-concept machine learning studies in the area of risk assessment, diagnostics and precision prescribing. Most of these studies primarily use clinical data, but ...

    Abstract Progress in developing personalised care for mental disorders is supported by numerous proof-of-concept machine learning studies in the area of risk assessment, diagnostics and precision prescribing. Most of these studies primarily use clinical data, but models might benefit from additional neuroimaging, blood and genetic data to improve accuracy. Combined, multimodal models might offer potential for stratification of patients for treatment. Clinical implementation of machine learning is impeded by a lack of wider generalisability, with efforts primarily focused on psychosis and dementia. Studies across all diagnostic groups should work to test the robustness of machine learning models, which is an essential first step to clinical implementation, and then move to prospective clinical validation. Models need to exceed clinicians' heuristics to be useful, and safe, in routine decision-making. Engagement of clinicians, researchers and patients in digitalisation and 'big data' approaches are vital to allow the generation and accessibility of large, longitudinal, prospective data needed for precision psychiatry to be applied into real-world psychiatric care.
    MeSH term(s) Humans ; Machine Learning ; Neuroimaging/methods ; Prospective Studies ; Psychiatry/methods ; Psychotic Disorders
    Language English
    Publishing date 2022-03-31
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 218103-4
    ISSN 1472-1465 ; 0007-1250
    ISSN (online) 1472-1465
    ISSN 0007-1250
    DOI 10.1192/bjp.2022.23
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  8. Book ; Online ; Thesis: Clinical data patterns supporting differential diagnosis of recent onset of depression and clinical high risk for psychosis

    Wosgien, Antonia [Verfasser] / Koutsouleris, Nikolaos [Akademischer Betreuer]

    2023  

    Author's details Antonia Wosgien ; Betreuer: Nikolaos Koutsouleris
    Keywords Medizin, Gesundheit ; Medicine, Health
    Subject code sg610
    Language English
    Publisher Universitätsbibliothek der Ludwig-Maximilians-Universität
    Publishing place München
    Document type Book ; Online ; Thesis
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  9. Book ; Online ; Thesis: Investigation of childhood trauma as a transdiagnostic risk factor using multimodal machine learning

    Popovic, David [Verfasser] / Koutsouleris, Nikolaos [Akademischer Betreuer]

    2023  

    Author's details David Popovic ; Betreuer: Nikolaos Koutsouleris
    Keywords Medizin, Gesundheit ; Medicine, Health
    Subject code sg610
    Language English
    Publisher Universitätsbibliothek der Ludwig-Maximilians-Universität
    Publishing place München
    Document type Book ; Online ; Thesis
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  10. Article: Präzisionspsychiatrie in der klinischen Praxis am Beispiel von Transition und psychosozialer Funktionsbeeinträchtigung in klinischen Hochrisikopatienten für Psychose

    Hahn, Lisa / Eberle, Christopher / Koutsouleris, Nikolaos

    Nervenheilkunde

    2023  Volume 42, Issue 9, Page(s) 635

    Language German
    Document type Article
    ZDB-ID 604504-2
    ISSN 0722-1541
    Database Current Contents Medicine

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