Machine yearning: How advances in computational methods lead to new insights about reactions to loss. (February 2022)

• Record Type: Journal Article
• Authors: Malgaroli, Matteo; Maccallum, Fiona; Bonanno, George A.
• Is Part Of: Current opinion in psychology. Volume 43(2022)
• Page Start: 13
• Page End: 17
• Publication Date: 2022-02
• Subjects: Grief -- Machine learning -- Computation -- Trajectories -- Networks; Psychology -- Periodicals; 150.5
• DOI: 10.1016/j.copsyc.2021.05.003
• Languages: English
• ISSNs: 2352-250X

Abstract

Abstract: The loss of a loved one is a potentially traumatic event that can result in disparate outcomes and symptom patterns. Machine learning methods offer computational tools to probe this heterogeneity and understand grief psychopathology in its complexity. In this article, we examine the latest contributions to the scientific study of bereavement reactions garnered through the use of computational methods. We focus on findings originating from trajectory modeling studies, as well as the recent insights originating from the network analysis of prolonged grief symptoms. We also discuss applications of artificial intelligence for the accurate identification of major depression and post-traumatic stress, as examples for their potential applications to the study of loss reactions. Highlights: Grief reactions are far more heterogeneous than conventional perspectives on bereavement assumed. Advances in computational modeling offer methods to probe grief heterogeneity. Latent growth modeling teases apart distinct longitudinal trajectories. Network analysis examines interconnected relations among individual grief symptoms. Combining these and other artificial intelligence–driven methods will help further illuminate the nature of grief.