MDA: An Intelligent Medical Data Augmentation Scheme Based on Medical Knowledge Graph for Chinese Medical Tasks

Shi, Bowen; Zhang, Lijuan; Huang, Jie; Zheng, Huilin; Wan, Jian; Zhang, Lei

doi:10.3390/app122010655

Cited by 6 publications

(3 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These methods can negatively affect the original semantics, sample diversity, and domain specificity. A novel medical data augmentation (MDA) method based on medical knowledge graphs was evaluated on Chinese NER and relational classification with good results [58].…”

Section: Named Entity Recognition Normalization and Linking For Loementioning

confidence: 99%

Exploring the Latest Highlights in Medical Natural Language Processing across Multiple Languages: A Survey

Shaitarova,

Zaghir,

Lavelli

et al. 2023

Yearb Med Inform

View full text Add to dashboard Cite

Objectives: This survey aims to provide an overview of the current state of biomedical and clinical Natural Language Processing (NLP) research and practice in Languages other than English (LoE). We pay special attention to data resources, language models, and popular NLP downstream tasks. Methods: We explore the literature on clinical and biomedical NLP from the years 2020-2022, focusing on the challenges of multilinguality and LoE. We query online databases and manually select relevant publications. We also use recent NLP review papers to identify the possible information lacunae. Results: Our work confirms the recent trend towards the use of transformer-based language models for a variety of NLP tasks in medical domains. In addition, there has been an increase in the availability of annotated datasets for clinical NLP in LoE, particularly in European languages such as Spanish, German and French. Common NLP tasks addressed in medical NLP research in LoE include information extraction, named entity recognition, normalization, linking, and negation detection. However, there is still a need for the development of annotated datasets and models specifically tailored to the unique characteristics and challenges of medical text in some of these languages, especially low-resources ones. Lastly, this survey highlights the progress of medical NLP in LoE, and helps at identifying opportunities for future research and development in this field.

show abstract

Section: Named Entity Recognition Normalization and Linking For Loementioning

confidence: 99%

Exploring the Latest Highlights in Medical Natural Language Processing across Multiple Languages: A Survey

Shaitarova,

Zaghir,

Lavelli

et al. 2023

Yearb Med Inform

View full text Add to dashboard Cite

show abstract

“…At present, the knowledge map of traditional Tibetan medicine formulas mainly has the following three purposes [15][16][17] .…”

Section: Application Prospectmentioning

confidence: 99%

Knowledge graph construction of traditional Tibetan medicine formulas

Zhuoma,

Jiayang,

Cai

et al. 2023

Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023)

View full text Add to dashboard Cite

In order to intuitively and succinctly reflect information, such as the dosage form and components of traditional Tibetan medicine prescriptions, and the correlation information between formulas and formulas, between formulas and traditional Tibetan medicine, we have collected and collated a certain amount of traditional Tibetan medicine formula data in this article, and constructed a knowledge map of traditional Tibetan medicine formulas. Through the construction of this atlas, traditional Tibetan medicine workers can intuitively display various direct or indirect related information of the formula, so as to facilitate a deeper understanding of the hidden knowledge in the formula information.

show abstract

“…Most information generated in the TCM diagnosis and treatment process is stored in unstructured text; therefore, NER has become the basis of downstream TCM research. These downstream tasks include TCM disease prediction based on various machine learning methods [1], [2], TCM prescription generation and recommendation [3], [4], [5], TCM question and answer [6], and TCM knowledge map construction [7], [8], [9]. In recent years, with the proposal VOLUME 11, 2023 This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Optimization-Based Ensemble Learning Method for Traditional Chinese Medicine Named Entity Recognition

Zhao,

Qian,

Liu

et al. 2023

IEEE Access

View full text Add to dashboard Cite

The importance of named entity identification in traditional Chinese medicine (TCM) as the basis for supporting downstream tasks is receiving increasing attention. Deep learning-based methods have been widely used for related tasks. However, most current methods do not deal well with two common TCM entity recognition problems: an unbalanced number of entities and sparse entities. To solve these problems, we propose an ensemble learning method based on dynamic optimization. In this study, we first use bidirectional encoder representations from transformers (BERT) to extract word vectors and then further extract features based on BERT-bidirectional long short-term memory (BiLSTM). Then, we dynamically adjust the entity class and fusion weights of ensemble learning according to the entity distribution and sparsity of each batch. Finally, the prediction results are output through the conditional random field (CRF) layer. This approach allows the model to dynamically focus on difficult samples and to improve the update weights of the most beneficial learning tasks. In addition, we introduce a reduction factor to reduce the magnitude of the parameter updates when the entities are sparse. This prevents the model from being unduly disrupted by nonentity information. Therefore, our model can effectively reduce the negative impact of unbalanced numbers of entities and sparse entities. The experimental results show that our model achieves the best results on a publicly available TCM entity recognition dataset and has a faster convergence rate than the baseline model. Compared to the baseline model BERT-BiLSTM-CRF, our method improves the F1-score by 0.56, further demonstrating its effectiveness.

show abstract

MDA: An Intelligent Medical Data Augmentation Scheme Based on Medical Knowledge Graph for Chinese Medical Tasks

Cited by 6 publications

References 50 publications

Exploring the Latest Highlights in Medical Natural Language Processing across Multiple Languages: A Survey

Exploring the Latest Highlights in Medical Natural Language Processing across Multiple Languages: A Survey

Knowledge graph construction of traditional Tibetan medicine formulas

A Dynamic Optimization-Based Ensemble Learning Method for Traditional Chinese Medicine Named Entity Recognition

Contact Info

Product

Resources

About