Papers › Accurate clinical and biomedical Named entity recognition at scale
Accurate clinical and biomedical Named entity recognition at scale
Kocaman, Veysel; Talby, David
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. Our NER implementation establishes new state-of-the-art accuracy on 7 of 8 well-known biomedical NER benchmarks and 3 clinical concept extraction challenges: 2010 i2b2/VA clinical concept extraction, 2014 n2c2 de-identification, and 2018 n2c2 medication extraction. Moreover, clinical NER models trained using this implementation outperform the accuracy of commercial entity extraction solutions, AWS Medical Comprehend and Google Cloud Healthcare API by a large margin (8.9% and 6.7% respectively), without using memory-intensive language models.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Named Entity Recognition (NER) | AnatEM | BertForTokenClassification (Spark NLP) | F1 | 91.65 | #5 of 5 | Archive leaderboard | report |
| Named Entity Recognition (NER) | BC4CHEMD | BertForTokenClassification (Spark NLP) | F1 | 94.39 | #1 of 7 | Archive leaderboard | report |
| Named Entity Recognition (NER) | BC5CDR | BertForTokenClassification (Spark NLP) | F1 | 90.89 | #5 of 16 | Archive leaderboard | report |
| Named Entity Recognition (NER) | BioNLP13-CG | BertForTokenClassification (Spark NLP) | F1 | 87.83 | #2 of 3 | Archive leaderboard | report |
| Named Entity Recognition (NER) | Species800 | BertForTokenClassification (Spark NLP) | F1 | 82.59 | #2 of 2 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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