Network-based artificial intelligence approaches for advancing personalized psychiatry

• Verfasst von: Rajan, Sivanesan [VerfasserIn]
• Verfasst von: Schwarz, Emanuel [VerfasserIn]
• Titel: Network-based artificial intelligence approaches for advancing personalized psychiatry
• Verf.angabe: Sivanesan Rajan, Emanuel Schwarz
• E-Jahr: 2024
• Jahr: December 2024
• Umfang: 9 S.
• Fussnoten: Erstmals veröffentlicht: 21. Juni 2024 ; Gesehen am 26.11.2024
• Titel Quelle: Enthalten in: American journal of medical genetics. Part B, Neuropsychiatric genetics
• Ort Quelle: Hoboken, NJ : Wiley-Liss, 2003
• Jahr Quelle: 2024
• Band/Heft Quelle: 195(2024), 8 vom: Dez., Artikel-ID e32997, Seite 1-9
• ISSN Quelle: 1552-485X
• DOI: doi:10.1002/ajmg.b.32997
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: artificial intelligence
• Sach-SW: machine learning
• Sach-SW: mental disorders
• Sach-SW: network science
• Sach-SW: networks
• Sach-SW: psychiatry
• K10plus-PPN: 1909511374

Abstract

Psychiatric disorders have a complex biological underpinning likely involving an interplay of genetic and environmental risk contributions. Substantial efforts are being made to use artificial intelligence approaches to integrate features within and across data types to increase our etiological understanding and advance personalized psychiatry. Network science offers a conceptual framework for exploring the often complex relationships across different levels of biological organization, from cellular mechanistic to brain-functional and phenotypic networks. Utilizing such network information effectively as part of artificial intelligence approaches is a promising route toward a more in-depth understanding of illness biology, the deciphering of patient heterogeneity, and the identification of signatures that may be sufficiently predictive to be clinically useful. Here, we present examples of how network information has been used as part of artificial intelligence within psychiatry and beyond and outline future perspectives on how personalized psychiatry approaches may profit from a closer integration of psychiatric research, artificial intelligence development, and network science.