Toward empathetic care: an LLM-based multi-intention recognition framework for mental health and complex medical queries

Yang, Dingkang, Wei, Jinjie, Hu, Ming, Liu, Jiyao, Liu, Lihao, Chen, Zhaoyu, Li, Mingcheng, He, Junjun, Zhou, Wei, Liu, Yang and Zhang, Lihua 2026. Toward empathetic care: an LLM-based multi-intention recognition framework for mental health and complex medical queries. IEEE Transactions on Affective Computing 10.1109/taffc.2026.3728254

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Abstract

Mental health has become a critical global concern, and many individuals now turn to conversational agents for support when facing emotional distress and complex health problems. In these high-stakes and emotionally sensitive settings, patients' self-descriptions are often long, fragmented, and interweave medical facts with psychological needs, lifestyle concerns, and urgent safety questions. Large Language Model (LLM)–based assistants must therefore not only answer clinical questions, but also accurately uncover the multiple underlying intentions that drive a patient's request, as a prerequisite for empathetic and trustworthy interaction. In this work, we propose MIRA, a multi-intention recognition and planning framework designed to understand complex patient intentions in mental health–related consultations. MIRA first introduces a two-stage intention recognition module that decomposes verbose queries into clinically aligned sub-queries using semantic matching with medical intent prototypes, including intents such as medical psychological support and lifestyle guidance. Next, a Dynamic Intent Orchestration (DIO) agent leverages clinical dependency modeling to plan execution sequences that respect medical best practices and safety considerations. Based on this plan, a multi-agent collaboration system with a Hierarchical Progressive Decision-making (HPD) agent synthesizes evidence across sequential diagnostic stages to generate context-aware advice that is suitable for emotionally sensitive scenarios. We evaluate MIRA on several real-world medical dialogue benchmarks. Both automated metrics and expert physician evaluations show that MIRA consistently outperforms existing different baselines in intent comprehension, clinical planning, and overall response quality.

Item Type:	Article
Date Type:	Published Online
Status:	In Press
Schools:	Schools > Computational & Mathematical Sciences Schools > Computer Science & Informatics
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	1949-3045
Last Modified:	07 Sep 2026 10:00
URI:	https://orca.cardiff.ac.uk/id/eprint/189421

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