Clinical Chinese Named Entity Recognition in Natural Language Processing | springerprofessional.de

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2023 | Book

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Clinical Chinese Named Entity Recognition in Natural Language Processing

Authors: Shuli Guo, Lina Han, Wentao Yang

Publisher: Springer Nature Singapore

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

This book introduces how to enhance the context capture ability of the model, improve the position information perception ability of the pretrained models, and identify and denoise the unlabeled entities. The Chinese medical named entity recognition is an important branch of the intelligent medicine, which is beneficial to mine the information hidden in medical texts and provide the medical entity information for clinical medical decision-making and medical classification. Researchers, engineers and post-graduate students in the fields of medicine management and software engineering.

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Table of Contents

Frontmatter

Chapter 1. Theoretical Basis

Abstract

The aim of NER is to extract entities with actual meaning from massive unstructured text (Zhang et al. in Procedia Comput Sci 183:212–220, 2021 [1]). In the clinical and medical domain, clinical NER recognizes and classifies medical terms in unstructured medical text records, including symptoms, examinations, diseases, drugs, treatments, operations, and body parts. As a combination of structured and unstructured texts, the rapidly growing biomedical literature contains a significant amount of useful biomedical information.

Shuli Guo, Lina Han, Wentao Yang

Chapter 2. Related Existed Models

Abstract

There are some related existed models of NLP such as Word Embedding, CRF, Deep Neural Networks et al. The simple and necessary introductions are listed as following.

Shuli Guo, Lina Han, Wentao Yang

Chapter 3. Medical Named Entity Recognition Models with the Attention Distraction Mechanism

Abstract

In this chapter, a medical NER model, which is simply called as BERT-Attention-SCLSTM-CRF, is proposed by adding extended input units based on BiLSTM with attention distraction mechanism.

Shuli Guo, Lina Han, Wentao Yang

Chapter 4. Transformer Entity Automatic Extraction Models in Multi-layer Soft Location Matching Format

Abstract

In this chapter, a multi-layer soft position matching format Transformer method is proposed to extract clinical medical entities. The new model is shown in Fig. 4.1. The proposed model consists of five parts: WordPiece preprocessing module, BERT module, multi-layer soft position matching module, word format Transformer, and fuzzy CRF module.

Shuli Guo, Lina Han, Wentao Yang

Chapter 5. Medical Named Entity Recognition Modelling Based on Remote Monitoring and Denoising

Abstract

The electronic medical records (EMRs) are used in the public data set provided by Yidu Cloud to obtain remote data sets through remote supervision. For the obtained remote data set, in order to improve the reliability of the data set, the PU learning is adapted for denoising to reduce the negative impacts of mislabeled negative samples or unlabeled samples of the model. Finally, the negative samples and the pretraining models are used to extract a cancer information.

Shuli Guo, Lina Han, Wentao Yang

Backmatter

Title: Clinical Chinese Named Entity Recognition in Natural Language Processing
Authors: Shuli Guo
Lina Han
Wentao Yang
Copyright Year: 2023
Publisher: Springer Nature Singapore
Electronic ISBN: 978-981-9926-65-7
Print ISBN: 978-981-9926-64-0
DOI: https://doi.org/10.1007/978-981-99-2665-7

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