ADAPTS: Agentic Decomposition for Automated Protocol-agnostic Tracking of Symptoms
Researchers introduce ADAPTS, an LLM-based framework that automates the assessment of depression and anxiety severity with expert-level precision. This protocol-agnostic system provides a foundation for objective and scalable psychiatric evaluation, especially in resource-limited settings.
Computer Science > Artificial Intelligence
Title: ADAPTS: Agentic Decomposition for Automated Protocol-agnostic Tracking of Symptoms
Modeling latent clinical constructs from unconstrained clinical interactions is a unique challenge in affective computing. We present ADAPTS (Agentic Decomposition for Automated Protocol-agnostic Tracking of Symptoms), a framework for automated rating of depression and anxiety severity using a mixture-of-agents LLM architecture.
This approach decomposes long-form clinical interviews into symptom-specific reasoning tasks, producing auditable justifications while preserving temporal and speaker alignment. Generalization was evaluated across two independent datasets (N=204) with distinct interview structures.
On high-discrepancy interviews, automated ratings approximated expert benchmarks (absolute error = 22) more closely than original human ratings (absolute error = 26). Implementing an "extended" protocol that incorporates qualitative clinical conventions significantly stabilized ratings, with absolute agreement reaching ICC(2,1) = 0.877.
These findings suggest that the ADAPTS framework enables promising evaluations of psychiatric severity. While the current implementation is purely text-based, the underlying architecture is readily extensible to multimodal inputs, including acoustic and visual features. By approximating expert-level precision in a protocol-agnostic manner, this framework provides a foundation for objective and scalable psychiatric assessment, especially in resource-limited settings.
Source: arXiv cs.AI Recent
