Traditional Chinese Medicine Automated Diagnosis Based on Knowledge Graph Reasoning

El‐Shafai, Walid; Mahmoud, Amira A.; El‐Rabaie, El‐Sayed M.; Taha, Taha E.; Zahran, O.; El‐Fishawy, Adel S.; Abd‐Elnaby, Mohammed; El‐Samie, Fathi E. Abd

doi:10.32604/cmc.2022.017295

Cited by 11 publications

(3 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At present, knowledge graphs have been widely used in various fields, such as recommendation systems [26][27], semantic search [28], knowledge question-answering [29], text reasoning [30][31], etc.…”

Section: Knowledge Graphsmentioning

confidence: 99%

A review of recommendation system research based on knowledge graph

Huang¹,

Yao²,

Wang³

2023

Third International Conference on Machine Learning and Computer Application (ICMLCA 2022)

View full text Add to dashboard Cite

Section: Knowledge Graphsmentioning

confidence: 99%

A review of recommendation system research based on knowledge graph

Huang¹,

Yao²,

Wang³

2023

Third International Conference on Machine Learning and Computer Application (ICMLCA 2022)

View full text Add to dashboard Cite

“…Furthermore, there is also some research about inference on the KG directly, without embedding the relations and entities. El-Shafai et al [ 26 ] provided a method that simulates syndrome differentiation through Bayes and TF-IDF on a knowledge graph to achieve automated diagnosis in TCM. Yao et al [ 27 ] presented an ontology-based model that utilized ontology attributes for training the neural network for medicine side-effect prediction.…”

Section: Related Workmentioning

confidence: 99%

Knowledge-Based Recurrent Neural Network for TCM Cerebral Palsy Diagnosis

Tian

et al. 2022

Evidence-Based Complementary and Alternative Medicine

View full text Add to dashboard Cite

Cerebral palsy is one of the most prevalent neurological disorders and the most frequent cause of disability. Identifying the syndrome by patients’ symptoms is the key to traditional Chinese medicine (TCM) cerebral palsy treatment. Artificial intelligence (AI) is advancing quickly in several sectors, including TCM. AI will considerably enhance the dependability and precision of diagnoses, expanding effective treatment methods’ usage. Thus, for cerebral palsy, it is necessary to build a decision-making model to aid in the syndrome diagnosis process. While the recurrent neural network (RNN) model has the potential to capture the correlation between symptoms and syndromes from electronic medical records (EMRs), it lacks TCM knowledge. To make the model benefit from both TCM knowledge and EMRs, unlike the ordinary training routine, we begin by constructing a knowledge-based RNN (KBRNN) based on the cerebral palsy knowledge graph for domain knowledge. More specifically, we design an evolution algorithm for extracting knowledge in the cerebral palsy knowledge graph. Then, we embed the knowledge into tensors and inject them into the RNN. In addition, the KBRNN can benefit from the labeled EMRs. We use EMRs to fine-tune the KBRNN, which improves prediction accuracy. Our study shows that knowledge injection can effectively improve the model effect. The KBRNN can achieve 79.31% diagnostic accuracy with only knowledge injection. Moreover, the KBRNN can be further trained by the EMRs. The results show that the accuracy of fully trained KBRNN is 83.12%.

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

Traditional Chinese Medicine Automated Diagnosis Based on Knowledge Graph Reasoning

Cited by 11 publications

References 19 publications

A review of recommendation system research based on knowledge graph

A review of recommendation system research based on knowledge graph

Knowledge-Based Recurrent Neural Network for TCM Cerebral Palsy Diagnosis

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

Contact Info

Product

Resources

About