InsightFlow：利用大语言模型从心理健康患者叙述中合成因果模型 / InsightFlow: LLM-Driven Synthesis of Patient Narratives for Mental Health into Causal Models
1️⃣ 一句话总结
这篇论文提出了一个名为InsightFlow的系统，它能够利用大语言模型自动分析心理治疗对话，并生成符合临床5P框架的因果模型图，其生成结果在结构和语义上与专家构建的模型具有可比性，展现了AI辅助临床决策的潜力。
arXiv 提交日期: 2026-04-14
Abstract - InsightFlow: LLM-Driven Synthesis of Patient Narratives for Mental Health into Causal Models
Clinical case formulation organizes patient symptoms and psychosocial factors into causal models, often using the 5P framework. However, constructing such graphs from therapy transcripts is time consuming and varies across clinicians. We present InsightFlow, an LLM based approach that automatically generates 5P aligned causal graphs from patient-therapist dialogues. Using 46 psychotherapy intake transcripts annotated by clinical experts, we evaluate LLM generated graphs against human formulations using structural (NetSimile), semantic (embedding similarity), and expert rated clinical criteria. The generated graphs show structural similarity comparable to inter annotator agreement and high semantic alignment with human graphs. Expert evaluations rate the outputs as moderately complete, consistent, and clinically useful. While LLM graphs tend to form more interconnected structures compared to the chain like patterns of human graphs, overall complexity and content coverage are similar. These results suggest that LLMs can produce clinically meaningful case formulation graphs within the natural variability of expert practice. InsightFlow highlights the potential of automated causal modeling to augment clinical workflows, with future work needed to improve temporal reasoning and reduce redundancy.
源自 arXiv: 2604.12721