AI-Based Support for Mental Health Communication (AIM-Health)

• Grant number: 24/10233-7
• Support Opportunities: Regular Research Grants
• Start date: February 01, 2025
• End date: January 31, 2028
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Helena de Medeiros Caseli
• Grantee: Helena de Medeiros Caseli
• Principal researcher abroad: Aline Villavicencio
• Institution abroad: University of Exeter, Exeter, England
• Principal researcher abroad: Rodrigo Souza Wilkens
• Institution abroad: Universitè Catolique de Louvain (UCL), Belgium
• Host Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil
• Associated researchers: Doretta Caramaschi ; Eloize Rossi Marques Seno ; Heloisa Cristina Figueiredo Frizzo ; Ivandre Paraboni ; Ke Li ; Kim Wright ; Sylvia Iasulaitis ; Taís Bleicher ; Vânia Paula de Almeida Neris
• Associated scholarship(s): 25/09978-0 - Investigating the use of metaphors to express symptoms of depression in Portuguese texts, BP.IC; 25/05366-0 - Module for identifying signs of depression considering figurative language, BP.TT; 25/05422-8 - Idiomatic language identification module in the mental health domain, BP.TT; 25/05410-0 - Active learning module to help the identification of signs of depression, BP.TT

Abstract

Depression is a mental health disorder that affects a large portion of the global population, being the second largest contributor to decrease in healthy life expectancy. Depression is characterized by a clinically significant form of psychological suffering that leads to significant impairment in someone's functionality, reduced quality of life and, in severe cases, can lead to death due to the risk of suicide. However, according to the World Health Organization, only a quarter of individuals suffering from mental health disorders receive proper care. Advances in Artificial Intelligence (AI) and Natural Language Processing (NLP) research have been developed to a level that can be used for proposing computational solutions that assist in the detection and intervention in mental health conditions. AI and NLP based solutions that aid in the identification of signs of depression can be useful both in individual treatment and in making public policy decisions. Similarly, solutions that offer autonomous, ethical, reliable, controlled, and engaging intervention, in real time, can help mitigate the damage caused by depression. This project works on proposing and developing AI and NLP based solutions for the detection and intervention of mental health conditions that can have a broader reach and allow mental health support to individuals and populations that would not otherwise have access to it. Furthermore, as social determinants are frequently mentioned as risk factors for mental health conditions, this project also aims at furthering the understanding about them in two contexts (Brazil and the United Kingdom). This project aims to address scientific challenges that are still present and very relevant in this context: (i) dealing with more abstract language (such as figurative language) commonly used in mental health self-narratives, and (ii) outputting personalized interventions suitable for an individual's context. (AU)