|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | The effects of lingual exercise in stroke patients with dysphagia显示文摘 | Robbins J Kays SA Gangnon RE | 2007 | Archives of Physi- cal Medicine and Rehabilitation2007，88，2： | 1 |
| 2 | Risk factors for surgical site infection following spine surgery:efficary of intraoperative saline irrigation显示文摘 | Watanabe M Sa Kai D Matsuyama D et a1 | 2010 | J Neurosurg Spine2010，12，5： | 1 |
| 3 | Integrity of articular cartilage on T2mapping associated with meniscal signal change 显示文摘 | Kai B Mann SA King C | 2011 | Eur J Radiol2011，79，： | 1 |
| 4 | Continuous dynamic recrystallization in magnesium alloy 显示文摘 | Gal iyev A Kaibyshev R Sa kai T | 2003 | Materials Science Forum2003，，： | 1 |
| 5 | A fibril-specific,conformation-dependent antibody recognizes a subset of Abeta plaques in Alzheimer disease,Down syndrome and Tg2576transgenic mouse brain显示文摘 | Sarsoza F Saing T Kayed R | | 0，，04： | 1 |
| 6 | The effects of lingual ex- ercise in stroke patients with dysphagia显示文摘 | Robbins J Kays SA Gangnon RE | 2007 | Archives of Physical Medicine and Rehabilitation2007，88，2： | 1 |
| 7 | Formation and characterization of microemulsions containing polymeric silicone显示文摘 | Suraj Chandra Sharma Koji Tsuchiya Kenichi Sa Kai | 2008 | Langmuir2008，24，： | 1 |
| 8 | The state of research and development for application of metal hydrides in Japan显示文摘 | Uehara I Sa Kai T Ishi Kawa H | 1997 | J Alloys Compd1997，253，254： | 1 |
| 9 | Text Mining and Analysis of Treatise on Febrile Diseases Based on Natural Language Processing显示文摘Objective:With using natural language processing (NLP) technology to analyze and process the text of 'Treatise on Febrile Diseases (TFDs)'for the sake of finding important information, this paper attempts to apply NLP in the field of text mining of traditional Chinese medicine (TCM)literature. Materials and Methods:Based on the Python language, the experiment invoked the NLP toolkit such as Jieba, nltk, gensim,and sklearn library, and combined with Excel and Word software. The text of 'TFDs' was sequentially cleaned, segmented, and moved the stopped words, and then implementing word frequency statistics and analysis, keyword extraction, named entity recognition (NER) and other operations, finally calculating text similarity. Results:Jieba can accurately identify the herbal name in 'TFDs.' Word frequency statistics based on the word segmentation found that 'warm therapy' is an important treatment of 'TFDs.' Guizhi decoction is the main prescription,and five core decoctions are identified. Keyword extraction based on the term 'frequency-inverse document frequency' algorithm is ideal.The accuracy of NER in 'TFDs' is about 86%;latent semantic indexing model calculating the similarity,'Understanding of Synopsis of Golden Chamber (SGC)' is much more similar with 'SGC' than with 'TFDs.' The results meet expectation. Conclusions:It lays a research foundation for applying NLP to the field of text mining of unstructured TCM literature. With the combination of deep learning technology,NLP as an important branch of artificial intelligence will have broader application prospective in the field of text mining in TCM literature and construction of TCM knowledge graph as well as TCM knowledge services. | Kai Zhao Na Shi Zhen Sa Hua-Xing Wang Chun-Hua Lu Xiao-Ying Xu | 2020 | World Journal of Traditional Chinese Medicine2020，6，1： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员