Electronic Medical Records (EMR) with unstructured sentences and various conceptual expressions
provide rich information for medical information extraction. However, common Named Entity Recognition (NER)
in Natural Language Processing (NLP) are not well suitable for clinical NER in EMR. This study aims at applying
neural networks to clinical concept extractions. We integrate Bidirectional Long Short-Term Memory Networks
(Bi-LSTM) with a Conditional Random Fields (CRF) layer to detect three types of clinical named entities. Word
representations fed into the neural networks are concatenated by character-based word embeddings and Contin-
uous Bag of Words (CBOW) embeddings trained both on domain and non-domain corpus. We test our NER
system on i2b2/VA open datasets and compare the performance with six related works, achieving the best result
of NER with F1 value 0.853 7. We also point out a few speciˉc problems in clinical concept extractions which will
give some hints to deeper studies.
QIN Ying (秦颖), ZENG Yingfei (曾颖菲)
. Research of Clinical Named Entity Recognition Based on Bi-LSTM-CRF[J]. Journal of Shanghai Jiaotong University(Science), 2018
, 23(3)
: 392
.
DOI: 10.1007/s12204-018-1954-5
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