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

Abstract

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.

Persistent clinical symptoms and their association with CM syndromes in post-COVID-19 rehabilitation patients in Hong Kong

“…These clinical texts recorded by CM practitioners would provide useful data to understand and manage patient heterogeneity. Currently, there is no “gold” standard of corpora for CM that could be adopted for use in analysing CM medical text data [ 54 ]. The unstructured nature and variable forms of Chinese text expressions makes data processing more difficult and hence requires additional manpower [ 54 ].…”

“…Currently, there is no “gold” standard of corpora for CM that could be adopted for use in analysing CM medical text data [ 54 ]. The unstructured nature and variable forms of Chinese text expressions makes data processing more difficult and hence requires additional manpower [ 54 ]. In the current study, the writing styles and habitual vocabularies were relatively less complex compared to other full text-mining analysis of complete CM medical records since this study only focused on CM Syndromes and clinical characteristics as recorded in the case report files of the previous observational study [ 14 ].…”

Application of Mathematical Modeling and Computational Tools in the Modern Drug Design and Development Process

“…Computer-aided drug design methods have been applied in the field of drug development over the past two decades [56]. Currently, this is seen as one of the best appropriate alternatives to high-throughput screening, which is routinely used in drug design and development.…”

CPMI-ChatGLM: parameter-efficient fine-tuning ChatGLM with Chinese patent medicine instructions

“…Currently, research on pre-trained language models (PLMs) in the domain of TCM mainly focuses on entity recognition, clinical record classification, and feature extraction 9 – 11 . Pan et al 12 conducted an in-depth study on electronic medical records (EMRs) in TCM and proposed a named entity recognition (NER) pipeline called the ALBERT-BiLSTM-CRF, which focuses on TCM orthopedic EMRs.…”