ADAPTS: Agentic Decomposition for Automated Protocol-agnostic Tracking of Symptoms

• URL: http://arxiv.org/abs/2605.03212v2
• Date: Wed, 06 May 2026 14:46:49 GMT
• Title: ADAPTS: Agentic Decomposition for Automated Protocol-agnostic Tracking of Symptoms
• Abstract summary: ADAPTS (Agentic Decomposition for Automated Protocol-agnostic Tracking of Symptoms) is a framework for automated rating of depression and anxiety severity.<n>This approach decomposes long-form clinical interviews into symptom-specific reasoning tasks.<n>Ratings approximated expert benchmarks more closely than original human ratings.
• Score: 36.94429692322632
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 ($\text{absolute error}=22$) more closely than original human ratings ($\text{absolute error}=26$). Implementing an ``extended'' protocol that incorporates qualitative clinical conventions significantly stabilized ratings, with absolute agreement reaching $\text{ICC(2,1)} = 0.877$. 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.

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