ClinLinker：西班牙语临床概念提及的医学实体链接
ClinLinker: Medical Entity Linking of Clinical Concept Mentions in Spanish
AI总结：
ClinLinker提出基于SapBERT双编码器检索与交叉编码器重排序的两阶段西班牙语医学实体链接方法，在DisTEMIST和MedProcNER上分别超越先前基准40和43个百分点。
摘要：
自然语言处理技术的进步，例如命名实体识别以及向UMLS或SNOMED-CT等广泛使用的标准化术语体系的规范化，再加上电子健康记录的数字化，已经显著推动了临床文本分析的发展。本研究提出了ClinLinker，这是一种采用两阶段流水线进行医学实体链接的新方法，该方法利用领域内适配语言模型在生物医学文本挖掘中的潜力：首先使用基于SapBERT的双编码器进行候选实体检索，随后使用交叉编码器进行重排序，该交叉编码器通过遵循对比学习策略进行训练，以适配西班牙语医学概念。该方法最初聚焦于西班牙语内容，显著优于为相同目的设计的多语言语言模型。即使在涉及异构医学术语体系的复杂场景下，并且仅在原始数据的一个子集上进行训练，这一结论依然成立。我们的结果使用top-k准确率（k=25）以及其他top-k指标进行评估，证明了我们的方法在两个不同的临床实体链接金标准语料库DisTEMIST（疾病）和MedProcNER（临床操作）上的性能，在DisTEMIST上比先前基准高出40个百分点，在MedProcNER上高出43个百分点，两者均规范化到SNOMED-CT代码。这些发现凸显了我们的方法处理语言特定细微差别的能力，并为实体链接设立了新的基准，为增强数字医疗记录的实用性提供了有力工具。所得系统具有实际价值，既可用于从临床记录中大规模自动生成结构化数据，也可用于穷尽式提取和协调预定义的临床关注变量。
英文摘要：
Advances in natural language processing techniques, such as named entity recognition and normalization to widely used standardized terminologies like UMLS or SNOMED-CT, along with the digitalization of electronic health records, have significantly advanced clinical text analysis. This study presents ClinLinker, a novel approach employing a two-phase pipeline for medical entity linking that leverages the potential of in-domain adapted language models for biomedical text mining: initial candidate retrieval using a SapBERT-based bi-encoder and subsequent re-ranking with a cross-encoder, trained by following a contrastive-learning strategy to be tailored to medical concepts in Spanish. This methodology, focused initially on content in Spanish, substantially outperforming multilingual language models designed for the same purpose. This is true even for complex scenarios involving heterogeneous medical terminologies and being trained on a subset of the original data. Our results, evaluated using top-k accuracy at 25 and other top-k metrics, demonstrate our approach's performance on two distinct clinical entity linking Gold Standard corpora, DisTEMIST (diseases) and MedProcNER (clinical procedures), outperforming previous benchmarks by 40 points in DisTEMIST and 43 points in MedProcNER, both normalized to SNOMED-CT codes. These findings highlight our approach's ability to address language-specific nuances and set a new benchmark in entity linking, offering a potent tool for enhancing the utility of digital medical records. The resulting system is of practical value, both for large scale automatic generation of structured data derived from clinical records, as well as for exhaustive extraction and harmonization of predefined clinical variables of interest.
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。
