Neural Disease Named Entity Extraction with Character-based BiLSTM+CRF in Japanese Medical Text

We propose an 'end-to-end' character-based recurrent neural network that extracts disease named entities from a Japanese medical text and simultaneously judges its modality as either positive or negative; i.e., the mentioned disease or symptom is affirmed or negated. The motivation to adopt neural networks is to learn effective lexical and structural representation features for Entity Recognition and also for Positive/Negative classification from an annotated corpora without explicitly providing any rule-based or manual feature sets. We confirmed the superiority of our method over previous char-based CRF or SVM methods in the results.

[1]  Nigel Collier,et al.  Bio-Medical Entity Extraction using Support Vector Machines , 2005, Artif. Intell. Medicine.

[2]  Wendy W. Chapman,et al.  ConText: An algorithm for determining negation, experiencer, and temporal status from clinical reports , 2009, J. Biomed. Informatics.

[3]  Peter L. Elkin,et al.  A controlled trial of automated classification of negation from clinical notes , 2005, BMC Medical Informatics Decis. Mak..

[4]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[5]  Prakash M. Nadkarni,et al.  Research Paper: Use of General-purpose Negation Detection to Augment Concept Indexing of Medical Documents: A Quantitative Study Using the UMLS , 2001, J. Am. Medical Informatics Assoc..

[6]  Tomoko Ohkuma,et al.  Overview of the NTCIR-10 MedNLP Task , 2013, NTCIR.

[7]  Eric Nichols,et al.  Named Entity Recognition with Bidirectional LSTM-CNNs , 2015, TACL.

[8]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[9]  Kazuhiko Ohe,et al.  Incorporating Knowledge Resources to Enhance Medical Information Extraction , 2013, NLPHealthcare@IJCNLP.

[10]  Deniz Yuret,et al.  CharNER: Character-Level Named Entity Recognition , 2016, COLING.

[11]  Kazuhiko Ohe,et al.  Extraction of Adverse Drug Effects from Clinical Records , 2010, MedInfo.

[12]  Guillaume Lample,et al.  Neural Architectures for Named Entity Recognition , 2016, NAACL.

[13]  Yuji Matsumoto,et al.  Japanese Named Entity Extraction with Redundant Morphological Analysis , 2003, NAACL.

[14]  Yuji Matsumoto,et al.  Chunking with Support Vector Machines , 2001, NAACL.

[15]  Wendy W. Chapman,et al.  A Simple Algorithm for Identifying Negated Findings and Diseases in Discharge Summaries , 2001, J. Biomed. Informatics.