LEVIS: A hypertension dataset in traditional Chinese medicine

Ou, Aihua; Lin, Xiaozhong; Li, Guozheng; You, Mingyu

doi:10.1109/bibm.2013.6732672

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…In this study, the initial data were collected from trained practitioners and clinical practitioners. Details of 928 cases of hypertension were collected from the clinical departments of the Guangdong Provincial Hospital, with both inpatient and outpatient medical records from the Liwan district [ 39 ]. All cases with incomplete information were removed from the data set, and the remaining 886 (95.47%) cases were used for analysis in this study.…”

Section: Resultsmentioning

confidence: 99%

Leveraging Representation Learning for the Construction and Application of a Knowledge Graph for Traditional Chinese Medicine: Framework Development Study

Weng¹,

Chen²,

Ou³

et al. 2022

JMIR Med Inform

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Background Knowledge discovery from treatment data records from Chinese physicians is a dramatic challenge in the application of artificial intelligence (AI) models to the research of traditional Chinese medicine (TCM). Objective This paper aims to construct a TCM knowledge graph (KG) from Chinese physicians and apply it to the decision-making related to diagnosis and treatment in TCM. Methods A new framework leveraging a representation learning method for TCM KG construction and application was designed. A transformer-based Contextualized Knowledge Graph Embedding (CoKE) model was applied to KG representation learning and knowledge distillation. Automatic identification and expansion of multihop relations were integrated with the CoKE model as a pipeline. Based on the framework, a TCM KG containing 59,882 entities (eg, diseases, symptoms, examinations, drugs), 17 relations, and 604,700 triples was constructed. The framework was validated through a link predication task. Results Experiments showed that the framework outperforms a set of baseline models in the link prediction task using the standard metrics mean reciprocal rank (MRR) and Hits@N. The knowledge graph embedding (KGE) multitagged TCM discriminative diagnosis metrics also indicated the improvement of our framework compared with the baseline models. Conclusions Experiments showed that the clinical KG representation learning and application framework is effective for knowledge discovery and decision-making assistance in diagnosis and treatment. Our framework shows superiority of application prospects in tasks such as KG-fused multimodal information diagnosis, KGE-based text classification, and knowledge inference–based medical question answering.

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Section: Resultsmentioning

confidence: 99%

Leveraging Representation Learning for the Construction and Application of a Knowledge Graph for Traditional Chinese Medicine: Framework Development Study

Weng¹,

Chen²,

Ou³

et al. 2022

JMIR Med Inform

Self Cite

View full text Add to dashboard Cite

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A Framework for Automated Knowledge Graph Construction Towards Traditional Chinese Medicine

Weng

Liu

Yan

et al. 2017

Health Information Science

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Leveraging Representation Learning for the Construction and Application of Knowledge Graph for Traditional Chinese Medicine (Preprint)

Weng¹,

Chen²,

Ou³

et al. 2022

Preprint

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BACKGROUND Knowledge discovery from treatment data records from prestigious Chinese physicians is a dramatic challenge in the applications of artificial intelligence models to the research of Traditional Chinese Medicine (TCM). OBJECTIVE This paper aims to construct a TCM knowledge graph from prestigious Chinese physicians and apply it to decision-making assistant of TCM diagnosis and treatment. METHODS A new framework leveraging a representation learning method for TCM knowledge graph construction and application is designed. A Transformer-based Contextualized Knowledge Graph Embedding (CoKE) model is applied to knowledge graph representation learning and knowledge distillation. Automatic identification and expansion of multi-hop relations are integrated with the CoKE model as a pipeline. Based on the framework, a TCM knowledge graph, containing 598,82 entities including diseases, symptoms, examinations, drugs, etc., 17 relations, and 604,700 triples, is constructed. The framework is validated through a link predication task. RESULTS Experiments show that the framework outperforms a set of baseline models in the link prediction task using standard metrics MRR and Hits@N. The knowledge embedding multi-tagged TCM discriminative diagnosis metrics also indicates the improvement of our framework compared with the baseline models CONCLUSIONS Experiments show that the clinical knowledge graph representation learning and application framework is effective for knowledge discovery and decision-making assistance in diagnosis and treatment . Our framework shows superiority of application prospects in tasks such as knowledge graph fused multi-modal information diagnosis, knowledge graph embedding based text classification and knowledge inference based medical question answering.

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LEVIS: A hypertension dataset in traditional Chinese medicine

Cited by 3 publications

References 7 publications

Leveraging Representation Learning for the Construction and Application of a Knowledge Graph for Traditional Chinese Medicine: Framework Development Study

Leveraging Representation Learning for the Construction and Application of a Knowledge Graph for Traditional Chinese Medicine: Framework Development Study

A Framework for Automated Knowledge Graph Construction Towards Traditional Chinese Medicine

Leveraging Representation Learning for the Construction and Application of Knowledge Graph for Traditional Chinese Medicine (Preprint)

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