Clinical Concept Extraction
9 papers with code • 1 benchmarks • 4 datasets
Automatic extraction of clinical named entities such as clinical problems, treatments, tests and anatomical parts from clinical notes.
( Source )
Benchmarks
These leaderboards are used to track progress in Clinical Concept Extraction
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Datasets
2010 i2b2/VA
Toronto NeuroFace Dataset
Most implemented papers
CharacterBERT: Reconciling ELMo and BERT for Word-Level Open-Vocabulary Representations From Characters
helboukkouri/character-bert
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COLING 2020
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 it not being intrinsically linked to the notion of Transformers.
Bidirectional LSTM-CRF for Clinical Concept Extraction
Extraction of concepts present in patient clinical records is an essential step in clinical research.
Bidirectional LSTM-CRF for Clinical Concept Extraction
Automated extraction of concepts from patient clinical records is an essential facilitator of clinical research.
Recurrent neural networks with specialized word embeddings for health-domain named-entity recognition
Nevertheless, the embeddings need to be retrained over datasets that are adequate for the domain, in order to adequately cover the domain-specific vocabulary.
Clinical Concept Extraction with Contextual Word Embedding
noc-lab/clinical_concept_extraction
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Next, a bidirectional LSTM-CRF model is trained for clinical concept extraction using the contextual word embedding model.
Embedding Strategies for Specialized Domains: Application to Clinical Entity Recognition
helboukkouri/acl_srw_2019
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ACL 2019
Using pre-trained word embeddings in conjunction with Deep Learning models has become the {``}de facto{''} approach in Natural Language Processing (NLP).
Improving Clinical Document Understanding on COVID-19 Research with Spark NLP
JohnSnowLabs/spark-nlp-workshop
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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.
CLIN-X: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domain
The field of natural language processing (NLP) has recently seen a large change towards using pre-trained language models for solving almost any task.
Accurate clinical and biomedical Named entity recognition at scale
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.
