[2605.03212] ADAPTS: Agentic Decomposition for Automated Protocol-agnostic Tracking of Symptoms
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Computer Science > Artificial Intelligence
arXiv:2605.03212 (cs)
[Submitted on 4 May 2026 (v1), last revised 6 May 2026 (this version, v2)]
Title:ADAPTS: Agentic Decomposition for Automated Protocol-agnostic Tracking of Symptoms
Authors:Alexandria K. Vail, Marcelo Cicconet, Katie Aafjes-van Doorn, Ryan Maroney, Marc Aafjes
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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.
Subjects:
Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC); Applications (stat.AP); Computation (stat.CO)
Cite as: arXiv:2605.03212 [cs.AI]
(or arXiv:2605.03212v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.03212
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arXiv-issued DOI via DataCite
Submission history
From: Alexandria Vail [view email] [v1] Mon, 4 May 2026 23:08:42 UTC (230 KB)
[v2] Wed, 6 May 2026 14:46:49 UTC (230 KB)
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