Clinical Concept Extraction

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

4 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

Most implemented papers archive 2025-07-28

9 shown of 9 papers with code (24 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• CharacterBERT: Reconciling ELMo and BERT for Word-Level Open-Vocabulary Representations From Characters
  20 Oct 2020 2 repositories listed Syntology ran 5 of 8 samples · 3 unverified
  Due to the compelling improvements brought by BERT, many recent representation models adopted the Transformer architecture as their main building block, consequently inheriting the wordpiece tokenization system despite…
• Accurate clinical and biomedical Named entity recognition at scale
  19 Jul 2022 1 repository listed
  We introduce an agile, production-grade clinical and biomedical Named entity recognition (NER) algorithm based on a modified BiLSTM-CNN-Char DL architecture built on top of Apache Spark.
• CLIN-X: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domain
  16 Dec 2021 1 repository listed
  The field of natural language processing (NLP) has recently seen a large change towards using pre-trained language models for solving almost any task.
• Improving Clinical Document Understanding on COVID-19 Research with Spark NLP
  7 Dec 2020 1 repository listed
  Second, the text processing pipeline includes assertion status detection, to distinguish between clinical facts that are present, absent, conditional, or about someone other than the patient.
• Embedding Strategies for Specialized Domains: Application to Clinical Entity Recognition
  1 Jul 2019 1 repository listed
  Using pre-trained word embeddings in conjunction with Deep Learning models has become the {``}de facto{''} approach in Natural Language Processing (NLP).
• Clinical Concept Extraction with Contextual Word Embedding
  24 Oct 2018 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  Next, a bidirectional LSTM-CRF model is trained for clinical concept extraction using the contextual word embedding model.
• Recurrent neural networks with specialized word embeddings for health-domain named-entity recognition
  29 Jun 2017 1 repository listed
  Nevertheless, the embeddings need to be retrained over datasets that are adequate for the domain, in order to adequately cover the domain-specific vocabulary.
• Bidirectional LSTM-CRF for Clinical Concept Extraction
  25 Nov 2016 1 repository listed
  Automated extraction of concepts from patient clinical records is an essential facilitator of clinical research.
• Bidirectional LSTM-CRF for Clinical Concept Extraction
  19 Oct 2016 1 repository listed
  Extraction of concepts present in patient clinical records is an essential step in clinical research.

Syntology lines on 2 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.