ADAPTS: Agentic Decomposition for Automated Protocol-agnostic Tracking of Symptoms
📰 ArXiv cs.AI
Learn how ADAPTS uses agentic decomposition and LLMs to automate symptom tracking in clinical interviews, improving affective computing
Action Steps
- Apply ADAPTS framework to clinical interview data using a mixture-of-agents LLM architecture
- Decompose long-form clinical interviews into symptom-specific reasoning tasks
- Configure the LLM model to produce auditable justifications for symptom severity ratings
- Test the ADAPTS framework on a dataset of clinical interviews to evaluate its performance
- Compare the results of ADAPTS with traditional symptom tracking methods to assess its effectiveness
Who Needs to Know This
Data scientists and researchers in affective computing and healthcare can benefit from this framework to improve automated symptom tracking and diagnosis
Key Insight
💡 ADAPTS framework uses a mixture-of-agents LLM architecture to decompose clinical interviews into symptom-specific reasoning tasks, enabling automated and auditable symptom severity rating
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🚀 ADAPTS: Automated symptom tracking using agentic decomposition & LLMs 🤖💻
Key Takeaways
Learn how ADAPTS uses agentic decomposition and LLMs to automate symptom tracking in clinical interviews, improving affective computing
Full Article
Title: ADAPTS: Agentic Decomposition for Automated Protocol-agnostic Tracking of Symptoms
Abstract:
arXiv:2605.03212v2 Announce Type: new 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 justifi
Abstract:
arXiv:2605.03212v2 Announce Type: new 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 justifi
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