ArticleBMC medical informatics and decision making2020
A semi-supervised approach for extracting TCM clinical terms based on feature words.
Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Digital intelligence technology: new quality productivity for precision traditional Chinese medicine.Frontiers in pharmacology · 2025Review
- Semantic-enhanced graph neural network for named entity recognition in ancient Chinese books.Scientific reports · 2024Article
- Natural Language Processing Applications for Computer-Aided Diagnosis in Oncology.Diagnostics (Basel, Switzerland) · 2023Review
- Identifying infected patients using semi-supervised and transfer learning.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Information Extraction from the Text Data on Traditional Chinese Medicine: A Review on Tasks, Challenges, and Methods from 2010 to 2021.Evidence-based complementary and alternative medicine : eCAM · 2022Review
- TCMNER and PubMed: A Novel Chinese Character-Level-Based Model and a Dataset for TCM Named Entity Recognition.Journal of healthcare engineering · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA semi-supervised model is proposed for extracting clinical terms of Traditional Chinese Medicine using feature words.
methodsThe extraction model is based on BiLSTM-CRF and combined with semi-supervised learning and feature word set, which reduces the cost of manual annotation and leverage extraction results.
resultsExperiment results show that the proposed model improves the extraction of five types of TCM clinical terms, including traditional Chinese medicine, symptoms, patterns, diseases and formulas. The best F1-value of the experiment reaches 78.70% on the test dataset.
conclusionsThis method can reduce the cost of manual labeling and improve the result in the NER research of TCM clinical terms.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.