A Method of Annotating Disease Names in TCM Patents Based on Co-training

Deng, Na; Chen, Xu; Chen, Xiong

doi:10.1007/978-3-030-33509-0_35

Cited by 1 publication

(1 citation statement)

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“…e recall rate and F1-score index of this model in the annotation of "Complete Library of Chinese Named Motions in Past Dynasties" are ahead of other models, and the algorithm may have better room for improvement. Deng et al [56,57,60] proposed a semisupervised learning method, using a small amount of data as an annotation for training, constructing a data set of first-hand Chinese herbal medicine names as the initial data set of the semisupervised learning loop, a serialized initial word segmentation, word length, pause words, valid words, and word structure as species different classifiers. e co-training method to extract disease names, medicine names, and four-character medication effect phrases from TCM patent text data.…”

Section: Machine Learning Models Shallow Machine Learningmentioning

confidence: 99%

Information Extraction from the Text Data on Traditional Chinese Medicine: A Review on Tasks, Challenges, and Methods from 2010 to 2021

Zhang

Huang

Wang

et al. 2022

Evidence-Based Complementary and Alternative Medicine

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Background. The practice of traditional Chinese medicine (TCM) began several thousand years ago, and the knowledge of practitioners is recorded in paper and electronic versions of case notes, manuscripts, and books in multiple languages. Developing a method of information extraction (IE) from these sources to generate a cohesive data set would be a great contribution to the medical field. The goal of this study was to perform a systematic review of the status of IE from TCM sources over the last 10 years. Methods. We conducted a search of four literature databases for articles published from 2010 to 2021 that focused on the use of natural language processing (NLP) methods to extract information from unstructured TCM text data. Two reviewers and one adjudicator contributed to article search, article selection, data extraction, and synthesis processes. Results. We retrieved 1234 records, 49 of which met our inclusion criteria. We used the articles to (i) assess the key tasks of IE in the TCM domain, (ii) summarize the challenges to extracting information from TCM text data, and (iii) identify effective frameworks, models, and key findings of TCM IE through classification. Conclusions. Our analysis showed that IE from TCM text data has improved over the past decade. However, the extraction of TCM text still faces some challenges involving the lack of gold standard corpora, nonstandardized expressions, and multiple types of relations. In the future, IE work should be promoted by extracting more existing entities and relations, constructing gold standard data sets, and exploring IE methods based on a small amount of labeled data. Furthermore, fine-grained and interpretable IE technologies are necessary for further exploration.

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Section: Machine Learning Models Shallow Machine Learningmentioning

confidence: 99%