The extraction and analysis of insights from medical data, primarily stored
in free-text formats by healthcare workers, presents significant challenges due
to its unstructured nature. Medical coding, a crucial process in healthcare,
remains minimally automated due to the complexity of medical ontologies and
restricted access to medical texts for training Natural Language Processing
models. In this paper, we proposed a method, "SNOBERT," of linking text spans
in clinical notes to specific concepts in the SNOMED CT using BERT-based
models. The method consists of two stages: candidate selection and candidate
matching. The models were trained on one of the largest publicly available
dataset of labeled clinical notes. SNOBERT outperforms other classical methods
based on deep learning, as confirmed by the results of a challenge in which it
was applied.

该研究提出了一种名为 “SNOBERT” 的方法，使用基于 BERT 的模型，将临床笔记中的文本片段与 SNOMED CT 中的特定概念进行链接，对医疗数据和医疗编码进行自动化处理，该方法在深度学习的基础上优于其他传统方法。

SNOBERT：SNOMED CT 临床术语中的临床记录实体关联基准

SNOBERT: A Benchmark for clinical notes entity linking in the SNOMED CT  clinical terminology

Whilst there has been growing progress in Entity Linking (EL) for general
language, existing datasets fail to address the complex nature of health
terminology in layman's language. Meanwhile, there is a growing need for
applications that can understand the public's voice in the health domain. To
address this we introduce a new corpus called COMETA, consisting of 20k English
biomedical entity mentions from Reddit expert-annotated with links to SNOMED
CT, a widely-used medical knowledge graph. Our corpus satisfies a combination
of desirable properties, from scale and coverage to diversity and quality, that
to the best of our knowledge has not been met by any of the existing resources
in the field. Through benchmark experiments on 20 EL baselines from string- to
neural-based models we shed light on the ability of these systems to perform
complex inference on entities and concepts under 2 challenging evaluation
scenarios. Our experimental results on COMETA illustrate that no golden bullet
exists and even the best mainstream techniques still have a significant
performance gap to fill, while the best solution relies on combining different
views of data.

COMETA 语料库是一个新的英文生物医学实体提及数据集，其对于医疗术语和语言的复杂性有很高的覆盖率和质量，作者通过 2 种不同的评估场景，对 20 种不同的实体链接方法进行测试，发现没有完美的方案，基于不同数据视角的特性融合是最佳方法。