NLP/LLM Computational Mental Health

Detecting Depression through Multi-Party Conversation Analysis

To detect mental problems such as depression based on analyzing behaviors in group discussion.

Semantic analysis + machine learning

Mr. Prasan Yapa

[Q1] Priyadarshana YHPP, Senanayake A, Liang Z, Piumarta I. (2024) Prompt Engineering for Digital Mental Health: A Short Review. Frontiers in Digital Health - Digital Mental Health 6:1410947. Doi: 10.3389/fdgth.2024.1410947. [SCI/Scopus/PubMed]
Yapa H. P. P. P., Liang Z, Piumarta I. (2024) ProDepDet: Out-Of-Domain Knowledge Transfer of Pre-Trained Large Language Models for Depression Detection in Text-Based Multi-Party Conversations. In the Proceedings of The International Joint Conference on Neural Networks (IJCNN 2024), Yokohama, Japan. [Scopus]
Priyadarshana, Y.H.P.P., Liang, Z., Piumarta, I. (2023). HelaDepDet: A Novel Multi-class Classification Model for Detecting the Severity of Human Depression. In: Takada, H., Marutschke, D.M., Alvarez, C., Inoue, T., Hayashi, Y., Hernandez-Leo, D. (eds) Collaboration Technologies and Social Computing. CollabTech 2023. Lecture Notes in Computer Science, vol 14199. Springer, Cham. [Scopus]
Yapa H. P. P. P., Liang Z, Piumarta I. (2023) Who Says What (WSW): A Novel Model for Utterance-Aware Speaker Identification in Text-Based Multi-Party Conversations. In Proceedings of the 19th International Conference on Web Information Systems and Technologies (WEBIST 2023), Rome, Italy. [Scopus]【Best Paper Award (= top 1%)】

To develop digital biomarkers for mental fatigue during programming learning.

Eye tracking + multimodal physiological sensing

Mr. Kristofer Kevin KOSASIH

Coming soon ...