  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.
