一种用于评估转诊合理性的无监督自然语言处理流水线
An Unsupervised Natural Language Processing Pipeline for Assessing Referral Appropriateness
AI总结：
针对意大利医疗体系中转诊原因仅为自由文本、缺乏标注数据的问题，研究提出基于意大利医疗文本预训练Transformer嵌入的无监督NLP流水线，在两类检查数据集上实现高精度的转诊原因识别与合理性评估，为区域医疗政策制定提供支持。
摘要：
研究目的：评估诊断转诊的合理性对于提升医疗效率、减少不必要的诊疗操作至关重要。但在意大利国家医疗服务体系（NHS）这类场景中，转诊原因仅以自由文本而非结构化编码记录，使得这项任务颇具挑战。为填补这一空白，我们提出了一套完全无监督的自然语言处理（NLP）流水线，无需依赖标注数据集即可提取并评估转诊原因。\n研究方法：我们的流水线利用在意大利医疗文本上预训练的基于Transformer的嵌入向量，对转诊原因进行聚类，并评估其与合理性指南的契合度。该流水线在无监督设置下运行，设计上可跨不同检查类型泛化。我们分析了来自意大利伦巴第大区的两个完整区域数据集，涵盖2019年至2021年间的所有转诊记录，分别为下肢静脉彩色多普勒超声检查（ECD；n=496971；用于模型开发）和软性内镜结肠镜检查（FEC；n=407949；仅用于测试）。针对两类检查，各随机抽取1000份转诊样本进行人工标注，用以衡量模型性能。\n研究结果：该流水线在识别转诊原因（精确率：ECD为92.43%、FEC为93.59%；召回率：ECD为83.28%、FEC为92.70%）和评估转诊合理性（精确率：ECD为93.58%、FEC为94.66%；召回率：ECD为91.52%、FEC为93.96%）方面均取得了优异性能。在区域层面，分析识别出了相关的不合理转诊群体以及不同场景下的差异，这些发现为伦巴第大区一项旨在强化指南依从性的新决议提供了依据。\n研究结论：本研究提出了一套鲁棒、可扩展的无监督NLP流水线，用于在大规模真实世界数据集中评估转诊合理性。研究证明了此类数据可被有效利用，为公共卫生部门提供了一款可部署的AI工具，用于监测诊疗实践并支持循证政策制定。
英文摘要：
Objective: Assessing the appropriateness of diagnostic referrals is critical for improving healthcare efficiency and reducing unnecessary procedures. However, this task becomes challenging when referral reasons are recorded only as free text rather than structured codes, like in the Italian NHS. To address this gap, we propose a fully unsupervised Natural Language Processing (NLP) pipeline capable of extracting and evaluating referral reasons without relying on labelled datasets. Methods: Our pipeline leverages Transformer-based embeddings pre-trained on Italian medical texts to cluster referral reasons and assess their alignment with appropriateness guidelines. It operates in an unsupervised setting and is designed to generalize across different examination types. We analyzed two complete regional datasets from the Lombardy Region (Italy), covering all referrals between 2019 and 2021 for venous echocolordoppler of the lower limbs (ECD;n=496,971; development) and flexible endoscope colonoscopy (FEC; n=407,949; testing only). For both, a random sample of 1,000 referrals was manually annotated to measure performance. Results: The pipeline achieved high performance in identifying referral reasons (Prec=92.43% (ECD), 93.59% (FEC); Rec=83.28% (ECD), 92.70% (FEC)) and appropriateness (Prec=93.58% (ECD), 94.66% (FEC); Rec=91.52% (ECD), 93.96% (FEC)). At the regional level, the analysis identified relevant inappropriate referral groups and variation across contexts, findings that informed a new Lombardy Region resolution to reinforce guideline adherence. Conclusions: This study presents a robust, scalable, unsupervised NLP pipeline for assessing referral appropriateness in large, real-world datasets. It demonstrates how such data can be effectively leveraged, providing public health authorities with a deployable AI tool to monitor practices and support evidence-based policy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
