LingYi: Medical Conversational Question Answering System based on Multi-modal Knowledge Graphs
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866909550359085056 |
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| author | Xia, Fei Li, Bin Weng, Yixuan He, Shizhu Liu, Kang Sun, Bin Li, Shutao Zhao, Jun |
| author_facet | Xia, Fei Li, Bin Weng, Yixuan He, Shizhu Liu, Kang Sun, Bin Li, Shutao Zhao, Jun |
| contents | The medical conversational system can relieve the burden of doctors and improve the efficiency of healthcare, especially during the pandemic. This paper presents a medical conversational question answering (CQA) system based on the multi-modal knowledge graph, namely "LingYi", which is designed as a pipeline framework to maintain high flexibility. Our system utilizes automated medical procedures including medical triage, consultation, image-text drug recommendation and record. To conduct knowledge-grounded dialogues with patients, we first construct a Chinese Medical Multi-Modal Knowledge Graph (CM3KG) and collect a large-scale Chinese Medical CQA (CMCQA) dataset. Compared with the other existing medical question-answering systems, our system adopts several state-of-the-art technologies including medical entity disambiguation and medical dialogue generation, which is more friendly to provide medical services to patients. In addition, we have open-sourced our codes which contain back-end models and front-end web pages at https://github.com/WENGSYX/LingYi. The datasets including CM3KG at https://github.com/WENGSYX/CM3KG and CMCQA at https://github.com/WENGSYX/CMCQA are also released to further promote future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2204_09220 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | LingYi: Medical Conversational Question Answering System based on Multi-modal Knowledge Graphs Xia, Fei Li, Bin Weng, Yixuan He, Shizhu Liu, Kang Sun, Bin Li, Shutao Zhao, Jun Computation and Language Artificial Intelligence The medical conversational system can relieve the burden of doctors and improve the efficiency of healthcare, especially during the pandemic. This paper presents a medical conversational question answering (CQA) system based on the multi-modal knowledge graph, namely "LingYi", which is designed as a pipeline framework to maintain high flexibility. Our system utilizes automated medical procedures including medical triage, consultation, image-text drug recommendation and record. To conduct knowledge-grounded dialogues with patients, we first construct a Chinese Medical Multi-Modal Knowledge Graph (CM3KG) and collect a large-scale Chinese Medical CQA (CMCQA) dataset. Compared with the other existing medical question-answering systems, our system adopts several state-of-the-art technologies including medical entity disambiguation and medical dialogue generation, which is more friendly to provide medical services to patients. In addition, we have open-sourced our codes which contain back-end models and front-end web pages at https://github.com/WENGSYX/LingYi. The datasets including CM3KG at https://github.com/WENGSYX/CM3KG and CMCQA at https://github.com/WENGSYX/CMCQA are also released to further promote future research. |
| title | LingYi: Medical Conversational Question Answering System based on Multi-modal Knowledge Graphs |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2204.09220 |