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Going deep into schizophrenia with artificial intelligence.
Cortes-Briones, Jose A; Tapia-Rivas, Nicolas I; D'Souza, Deepak Cyril; Estevez, Pablo A.
Afiliación
  • Cortes-Briones JA; Schizophrenia and Neuropharmacology Research Group, VA Connecticut Healthcare System, West Haven, CT, USA; Abraham Ribicoff Research Facilities, Connecticut Mental Health Center, New Haven, CT, USA; Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA. Electronic address:
  • Tapia-Rivas NI; Department of Electrical Engineering, University of Chile, Santiago, Chile.
  • D'Souza DC; Schizophrenia and Neuropharmacology Research Group, VA Connecticut Healthcare System, West Haven, CT, USA; Abraham Ribicoff Research Facilities, Connecticut Mental Health Center, New Haven, CT, USA; Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.
  • Estevez PA; Department of Electrical Engineering, University of Chile, Santiago, Chile.
Schizophr Res ; 245: 122-140, 2022 07.
Article en En | MEDLINE | ID: mdl-34103242
Despite years of research, the mechanisms governing the onset, relapse, symptomatology, and treatment of schizophrenia (SZ) remain elusive. The lack of appropriate analytic tools to deal with the heterogeneity and complexity of SZ may be one of the reasons behind this situation. Deep learning, a subfield of artificial intelligence (AI) inspired by the nervous system, has recently provided an accessible way of modeling and analyzing complex, high-dimensional, nonlinear systems. The unprecedented accuracy of deep learning algorithms in classification and prediction tasks has revolutionized a wide range of scientific fields and is rapidly permeating SZ research. Deep learning has the potential of becoming a valuable aid for clinicians in the prediction, diagnosis, and treatment of SZ, especially in combination with principles from Bayesian statistics. Furthermore, deep learning could become a powerful tool for uncovering the mechanisms underlying SZ thanks to a growing number of techniques designed for improving model interpretability and causal reasoning. The purpose of this article is to introduce SZ researchers to the field of deep learning and review its latest applications in SZ research. In general, existing studies have yielded impressive results in classification and outcome prediction tasks. However, methodological concerns related to the assessment of model performance in several studies, the widespread use of small training datasets, and the little clinical value of some models suggest that some of these results should be taken with caution.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Esquizofrenia / Inteligencia Artificial Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Schizophr Res Asunto de la revista: PSIQUIATRIA Año: 2022 Tipo del documento: Article Pais de publicación: Países Bajos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Esquizofrenia / Inteligencia Artificial Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Schizophr Res Asunto de la revista: PSIQUIATRIA Año: 2022 Tipo del documento: Article Pais de publicación: Países Bajos