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.

这项研究通过使用一种新颖的双阶段流程来实现医学实体链接，该流程利用领域内适应的语言模型进行生物医学文本挖掘，从而显著提升了临床文本分析的性能。该方法在西班牙语内容的初期侧重下，在涉及异构医学术语并在原始数据子集上进行训练的复杂场景中，显著优于为同一目的设计的多语言语言模型。通过在两个不同的临床实体链接 Gold Standard 语料库（DisTEMIST（疾病）和 MedProcNER（临床程序））上进行评估，我们的结果表明，该方法在 25 个 Top-k 准确度和其他 Top-k 指标上的性能优于前期基准，DisTEMIST 上提高了 40 个点，MedProcNER 上提高了 43 个点，两者均归一化到 SNOMED-CT 代码。这些发现突显了我们的方法在处理语言特定细微差别方面的能力，并树立了一个新的实体链接基准，为提高数字医疗记录的实用性提供了一种强大工具。该系统的结果具有实际价值，既可以用于从临床记录中自动产生大规模结构化数据，也可用于全面提取和协调预定义的临床变量。

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

Despite the widespread success of self-supervised learning via masked
language models (MLM), accurately capturing fine-grained semantic relationships
in the biomedical domain remains a challenge. This is of paramount importance
for entity-level tasks such as entity linking where the ability to model entity
relations (especially synonymy) is pivotal. To address this challenge, we
propose SapBERT, a pretraining scheme that self-aligns the representation space
of biomedical entities. We design a scalable metric learning framework that can
leverage UMLS, a massive collection of biomedical ontologies with 4M+ concepts.
In contrast with previous pipeline-based hybrid systems, SapBERT offers an
elegant one-model-for-all solution to the problem of medical entity linking
(MEL), achieving a new state-of-the-art (SOTA) on six MEL benchmarking
datasets. In the scientific domain, we achieve SOTA even without task-specific
supervision. With substantial improvement over various domain-specific
pretrained MLMs such as BioBERT, SciBERTand and PubMedBERT, our pretraining
scheme proves to be both effective and robust.

本研究提出了一种自我对齐的预训练模型 SapBERT，以处理医学名词实体链接（MEL）等实体级任务，利用 UMLS 等生物医学本体学的庞大集合，实现了新的最优结果，并证明其相对于 BioBERT 和 PubMedBERT 等先前的特定领域的 MLM 的有效性和鲁棒性。