Multimodal Deep Learning for Early Detection of Depression and Anxiety through Explainable AI
Year: 2025
Type: article
Abstract: Mental illness, particularly depression and anxiety, is a leading cause of global disease burden. Underdiagnosis is common due to misperceptions and negative stigma around mental health, limited resources, and self-reporting bias. Newer multimodal deep learning (MDL) frameworks have demonstrated the ability to distill behavioral, linguistic, and physiological signals pertaining to mental health from a number of data streams. However, uptake in clinical practice has been limited partly due to lack of transparency in how the models reach their conclusions. This study proposes a multimodal deep learning framework for the automatic early detection of anxiety and depression from text, audio and video signals with a special focus on Explainable AI(XAI). Basing the research on the benchmark datasets DAIC-WOZ, E-DAIC, and eRisk, the model outperformed unimodal baselines, and delivered clinically meaningful results that were interpretable. The research shows that leveraging explanatory artificial intelligence with MDL frameworks can create a more reliable and transparent AI-based screening tool for mental health problems.
Author: Nallapalli, Reuel Stefan
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7087748248
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