Exploring Relational Agents for Different Healthcare Applications

Relational agents (RAs) are a special type of computer program or virtual entity designed to interact with humans in a way that simulates social interactions. These agents are equipped with artificial intelligence (AI) and natural language processing capabilities, allowing them to engage in conversations, interpret emotions, and respond with empathetic and contextually appropriate behaviors. They play a pivotal role in human-computer interaction, particularly in fields like healthcare, where personalized and compassionate communication is crucial.

In this research, different aspects and applications of RAs are explored in the domain of healthcare services. Our earlier explorations target the efficacy, acceptance, usability, and other basic measurements regarding RAs for healthcare services, particularly during COVID-19. Currently, we are investigating future opportunities for employing RAs in diverse healthcare applications, including gestational diabetes, different epidemics, health education, etc. Moreover, we are also working toward achieving universal health coverage (UHC) in Bangladesh by utilizing RAs that have the capability of interacting using Bangla languages. These research works are exclusively and jointly conducted with Data and Design Nest at the University of Louisiana at Lafayette, USA. Outcomes of this initiative have been published in ACM UIST 2022, ACM HAI 2021, IEEE ISCC 2023, JMIR Human Factors, IJERPH, PervasiveHealth 2021, and DESRIST 2021.

Predicting Association Between Entities in Heterogeneous Biological Networks

Heterogeneity is inherent in biological networks which consist of different entities as nodes (i.e., genes, diseases, drugs, function) and represent the relationships between these entities as edges. Predicting potential associations between biological entities currently has been an important problem in biomedical research. In general, a deep learning model uses the contextual information and structures of the heterogeneous networks to identify the associations. This project will utilize powerful tools, e.g., GNN & MRF, to develop a more accurate, explainable model for link predictions in heterogeneous networks. Dr. Azad Abul Kalam will collaborate with us on this project.