- •Easy to use, scalable NLP framework that can leverage Spark.
- •Introduction of BERT based Relation Extraction models.
- •State-of-the-art performance on Named Entity Recognition and Relation Extraction.
- •Reported SOTA performance of multiple public benchmark datasets.
- •Application of these models on real-world use-cases.
|Current code version||v1.0|
|Permanent link to code/repository used for this code version||https://github.com/SoftwareImpacts/SIMPAC-2022-44|
|Permanent link to Reproducible Capsule||https://codeocean.com/capsule/1636734/tree/v1|
|Legal Code License||Apache License, 2.0|
|Code versioning system used||git|
|Software code languages, tools, and services used||python, scala, java|
|Compilation requirements, operating environments & dependencies||Windows or Linux, JVM, Spark|
|If available Link to developer documentation/manual||https://nlp.johnsnowlabs.com/docs/en/licensed_install|
|Support email for questions||[email protected]|
3.1 Prescription parsing
3.2 Adverse drug event detection
3.3 Putting facts on a timeline
3.4 Generating knowledge graph
3.5 Improved entity mapping
4. Impact overview
Declaration of Competing Interest
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- BERT: Pre-training of deep bidirectional transformers for language understanding.CoRR. 2018; (abs/1810.04805)
- Two are better than one: Joint entity and relation extraction with table-sequence encoders.CoRR. 2020; (abs/2010.03851, https://arxiv.org/abs/2010.03851)
- BioBERT: A pre-trained biomedical language representation model for biomedical text mining.CoRR. 2019; (abs/1901.08746)
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- Improving clinical document understanding on COVID-19 research with spark NLP.CoRR. 2020; (abs/2012.04005, https://arxiv.org/abs/2012.04005)
- Mining adverse drug reactions from unstructured mediums at scale.CoRR. 2022; (abs/2201.01405, https://arxiv.org/abs/2201.01405)
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- Understanding COVID-19 news coverage using medical NLP.2022 (arXiv, https://arxiv.org/abs/2203.10338)
- Preparing for the next pandemic: Transfer learning from existing diseases via hierarchical multi-modal BERT models to predict COVID-19 outcomes.2021
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- MSCAT: A machine learning assisted catalog of metabolomics software tools.Metabolites. 2021; 11: 678
- MNLP at MEDIQA 2021: Fine-tuning PEGASUS for consumer health question summarization.in: Proceedings of the 20th Workshop on Biomedical Language Processing. 2021: 320-327 (https://aclanthology.org/2021.bionlp-1.37)
The code (and data) in this article has been certified as Reproducible by Code Ocean: (https://codeocean.com/). More information on the Reproducibility Badge Initiative is available at https://www.elsevier.com/physical-sciences-and-engineering/computer-science/journals.
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