Cognitive behavioural therapy without medication for schizophrenia

Bighelli, Irene; Çıray, Oğulcan; Salahuddin, Nurul Husna; Leucht, Stefan

doi:10.1002/14651858.cd015332.pub2

Cited by 1 publication

(1 citation statement)

References 80 publications

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“…Indeed, reports from 10 countries estimated schizophrenia-related costs per person per year to be around $2,004-94,229 US dollars, with considerable variability amongst countries [4]. Despite several treatments being available such as antipsychotics (dopamine receptor antagonists and partial agonists), up to 20-30% patients will remain treatment-resistant and further approaches such as cognitive behavioral therapy will be used as adjuncts [5][6][7]. Various studies have explored the diverging clinical presentations of schizophrenia patients and developed complexity estimators to aid clinicians in understanding the neuropathological processes involved in this complex illness [8,9].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Intersection of Schizophrenia, Machine Learning, and Genomics: A Scoping Review (Preprint)

Hudon,

Beaudoin,

Phraxayavong

et al. 2024

Preprint

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BACKGROUND An increasing body of literature highlights the integration of machine learning with genomic data in psychiatry, particularly for complex mental health disorders such as schizophrenia. These advanced techniques offer promising potential for uncovering various facets of these disorders. A comprehensive review of the current applications of machine learning in conjunction with genomic data within this context can significantly enhance our understanding of the current state of research and its future directions. OBJECTIVE The objective of this study is to conduct a systematic scoping review of the use of machine learning algorithms with genomic data in the field of schizophrenia. METHODS A systematic, scoping review, search was performed in the electronic databases of Medline, Web of Science, PsycNet (PsycINFO), and Google Scholar from 2013 to 2024. Studies at the intersection of schizophrenia, genomic data, and machine learning were evaluated. RESULTS The literature search identified 2437 eligible articles after removing duplicates. Following abstract screening, 143 full-text articles were assessed, and 121 were subsequently excluded. Therefore, 21 studies were thoroughly assessed. Various machine learning algorithms were used in the identified studies, with support vector machines being the most common. The studies notably used genomic data to predict schizophrenia, identifying schizophrenia features, discovering drugs, classifying schizophrenia amongst other mental health disorders, and predicting the quality-of-life of patients. CONCLUSIONS Several high-quality studies were identified. Yet, the application of machine learning with genomic data in the context of schizophrenia remains limited. Future research is essential to further evaluate the portability of these models and to explore their potential clinical applications.

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