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HBot: A Chatbot for Healthcare Applications in Traditional Chinese Medicine Based on Human Body 3D Visualization

arXiv 2408.00481 ↗First posted 1 Aug 2024cs.AIcited by 0

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Abstract (arXiv)

The unique diagnosis and treatment techniques and remarkable clinical efficacy of traditional Chinese medicine (TCM) make it play an important role in the field of elderly care and healthcare, especially in the rehabilitation of some common chronic diseases of the elderly. Therefore, building a TCM chatbot for healthcare application will help users obtain consultation services in a direct and natural way. However, concepts such as acupuncture points (acupoints) and meridians involved in TCM always appear in the consultation, which cannot be displayed intuitively. To this end, we develop a healthcare chatbot (HBot) based on a human body model in 3D and knowledge graph, which provides conversational services such as knowledge Q&A, prescription recommendation, moxibustion therapy recommendation, and acupoint search. When specific acupoints are involved in the conversations between user and HBot, the 3D body will jump to the corresponding acupoints and highlight them. Moreover, Hbot can also be used in training scenarios to accelerate the teaching process of TCM by intuitively displaying acupuncture points and knowledge cards. The demonstration video is available at https://www.youtube.com/watch?v=UhQhutSKkTU . Our code and dataset are publicly available at Gitee: https://gitee.com/plabrolin/interactive-3d-acup.git

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 4 of its 16 references that are in the map. First posted on arXiv 1 Aug 2024.

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4 of 16 references.

References · 4

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  2. 2019DocRED: A Large-Scale Document-Level Relation Extraction Datasetcited by 239
  3. 2021ESimCSE: Enhanced Sample Building Method for Contrastive Learning of Unsupervised Sentence Embeddingcited by 73
  4. 2022A Benchmark for Automatic Medical Consultation System: Frameworks, Tasks and Datasetscited by 45

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