用大模型自动生成心理治疗中的因果分析图，提升临床建模效率。
InsightFlow: LLM-Driven Synthesis of Patient Narratives for Mental Health into Causal Models
- 基于大模型解析对话，自动生成符合5P框架的因果图
- 生成图与专家标注结构相似度达人类标注者间水平
- 适合临床辅助决策与医疗数据智能分析场景
临床个案构想将患者症状与心理社会因素组织为因果模型，常采用5P框架。但从治疗记录中构建此类图谱耗时且存在临床差异。本文提出InsightFlow，一种基于大语言模型的方法，可从患者-治疗师对话中自动生成符合5P框架的因果图。使用46份经临床专家标注的初次治疗录音进行评估，通过结构（NetSimile）、语义（嵌入相似性）及专家评分标准对比生成图与人工图。结果表明，生成图在结构上与人类标注者间一致性相当，语义上高度匹配。专家评价认为输出内容中等完整、一致且具临床价值。尽管生成图比人类图更互联，整体复杂度和内容覆盖范围相近。这说明大模型可在专家实践自然变异性范围内生成有意义的个案构图。InsightFlow展示了自动化因果建模在临床工作流中的潜力，未来需改进时间推理能力并减少冗余。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。