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智能中醫信息處理技術與應用(英文版) 版權信息
- ISBN：9787302582861
- 條形碼：9787302582861 ; 978-7-302-58286-1
- 裝幀：一般膠版紙
- 冊數：暫無
- 重量：暫無
- 所屬分類：>
智能中醫信息處理技術與應用(英文版) 本書特色
適讀人群 ：大眾本書是作者團隊在中醫領域進行了10余年的應用研究，在國家十五、十一五、十二五及十三五計劃項目支持下，形成的中醫醫案處理技術、中藥方劑挖掘技術、基于開放知識源及中醫文獻的知識獲取技術以及中醫智能輔助診斷系統等技術論文的集成，希望能為從事中醫信息化技術學習和研究的國內外相關研究人員、研究生及本科生提供借鑒，也為弘揚中華傳統醫學做出貢獻。
智能中醫信息處理技術與應用(英文版) 內容簡介
The past decades have witnessed the rapid advancements of computational intelligence techniques, including big data, machine learning, and knowledge engineering, in both industrial and academic communities. Specifically, with the diffusion of some computing paradigms such as natural language processing, knowledge graph, reasoning decision, it promotes the computer-assisted diagnosis and treatment in Traditional Chinese Medicine (TCM). Through the integration of our research achievements in the field of intelligent information processing on TCM over the last decade, this book introduces the data processing technologies in TCM medical records and TCM medication, the medical records-based knowledge acquisition, the text-based knowledge acquisition, and the applications of TCM knowledge. We would like to provide a guidance for graduate students, university teachers and professional technicians engaged in knowledge engineering and TCM informatization.
智能中醫信息處理技術與應用(英文版) 目錄
Contents
1 Data Processing Technology in TCM Records 1
1.1 Structural Technology Research on Symptom Data 1
1.1.1 Analyze the Symptoms 2
1.1.2 Structure the Symptoms 4
1.1.3 Conclusions 7
1.2 Semantic Feature Expansion Technology Based on Knowledge Graph 7
1.2.1 Knowledge Graph and Feature Acquisition Analysis 8
1.2.2 Symptom Normalization in TCM 9
1.2.3 Acquisition of Semantic Features Based on Knowledge Path 13
1.2.4 Experiment Analysis 16
1.2.5 Conclusions 21
1.3 Medical Case Retrieval Method Based on Machine Learning 22
1.3.1 Medical Record Representation 22
1.3.2 Case Retrieval Based on Learning Ranking 25
1.3.3 Experiment and Analysis 28
1.3.4 Conclusions 32
2 Data Processing Technology in TCM Medication 33
2.1 An Intelligent Medication Matching Method for TCM 33
2.1.1 Measure the Correlation between Medications 33
2.1.2 Random Walk Similarity of Nodes 37
2.1.3 The Graph Clustering 39
2.1.4 Experiment 39
2.2 The Core Medications Analysis Based on Social Network Analysis 41
2.2.1 The Social Network Construction about Semantic Relations of
TCM Records 41
2.2.2 Core Medications Analysis Based on Social Network Analysis 42
2.2.3 The Implementation of Core Medications Algorithms 46
2.2.4 Conclusions 48
2.3 Analysis and Mining of Core Prescription Using Fuzzy Cognitive Map 48
2.3.1 Construction of Fuzzy Cognitive Map 49
2.3.2 Realization of Core Prescription Mining 51
2.3.3 Systematic Review 55
2.3.4 Conclusions 57
3 The Medical Records-based Knowledge Acquisition 59
3.1 Centrality Research on the Traditional Chinese Medicine Network 59
3.1.1 Basic Thought and Concept 60
3.1.2 Method to Calculate Betweenness Centrality 62
3.1.3 Betweenness Centrality Algorithm 63
3.1.4 Example Analyses 64
3.1.5 Conclusions 66
3.2 Cognitive Induction Based Knowledge Acquisition 66
3.2.1 Data Preprocessing 66
3.2.2 Inductive Logic Based Inductive Learning Algorithm 68
3.2.3 Graph-based Inductive Learning Algorithm 71
3.2.4 Application of Inductive Learning Algorithm 73
3.3 Analysis on Interactive Structure of Knowledge Acquisition 77
3.3.1 Relevant Work 78
3.3.2 Structural Modeling Analyzing 79
3.3.3 Construction of Structural Model 81
3.3.4 Algorithms 81
3.3.5 Verification & Application 82
3.3.6 Conclusions 84
3.4 Application of Structural Analysis in Knowledge Acquisition of
Traditional Chinese Medicine 84
3.4.1 Structural Modeling 85
3.4.2 Arithmetic and Analysis 87
3.4.3 Application Example 88
3.4.4 Conclusions 91
4 Text-based Knowledge Acquisition 93
4.1 Knowledge Acquisition Based on Open Data Source 93
4.2 Unsupervised TCM Text Segmentation Combined with Domain Dictionary 101
4.2.1 Related Work 102
4.2.2 Method 103
4.2.3 Experience 106
4.2.4 Conclusions 109
4.3 A Phrase Mining Method for TCM 110
4.3.1 Methods 110
4.3.2 Results 115
4.3.3 Conclusions 117
4.4 Improving Distantly-Supervised Named Entity Recognition 117
4.4.1 Related work 119
4.4.2 NER Scheme 120
4.4.3 Experiment 127
4.4.4 Relation Extraction Frame 132
4.5 Nested Named Entity Recognition Method 133
4.5.1 Methodology 135
4.5.2 Experiments 137
4.5.3 Conclusions 141
5 Application of Knowledge of TCM 143
5.1 Fuzzy Ontology Constructing and its Application in TCM 143
5.1.1 Structure of Fuzzy Ontology 143
5.1.2 Application of Fuzzy Ontology 147
5.1.3 Conclusions 150
5.2 Personalized Diagnostic Modal Discovery of TCM Knowledge Graph 150
5.2.1 Access to Medical Data and Normalization 150
5.2.2 Obtain the Medical Records Node and Get the Path and Storage 153
5.2.3 Overlay All Medical Path Results 157
5.2.4 Using the Template 159
5.2.5 Result Analysis 160
5.2.6 Conclusions 168
5.3 Assistant Diagnostic Method of TCM 168
5.3.1 Data Pretreatment 169
5.3.2 Research on Integrated Diagnosis Based on Multi Classification 170
5.3.3 Conclusions 176
5.4 Auxiliary Diagnosis Based on the Knowledge Graph of TCM Syndrome 177
5.4.1 Related Work 177
5.4.2 TCM Diagnosis Path Discovery 181
5.4.3 Meta-path Based on Reasoning Strategy 182
?
5.4.4 Experiment 186
5.4.5 Conclusions 189
References 191
Figure List 195
Table List 199
展開全部
智能中醫信息處理技術與應用(英文版) 作者簡介
阿孜古麗·吾拉木，北京科技大學計算機與通信工程學院教授，博導；北京科技大學材料領域知識工程北京市重點實驗室副主任，主要研究方向為知識工程、知識圖譜、深度學習、人工智能。近年來，結合類腦智能技術，從感知的注意力機制、記憶學習以及推理技術等角度，研究形成自然語言實體與關系提取技術、大規模知識圖譜、知識庫構造與推理技術，以及人工智能知識工程應用技術。承擔國家863、國家科技支撐、國家重點研發計劃以及北京市省部級課題等30余項。組織實施了北京市科委重大項目“重點行業信息化知識庫建設”、研發“大數據征信服務平臺”、“工業大數據平臺”及“大數據驅動智能診斷系統”等，承擔科技部、北京市科委條件平臺建設，參與多個智慧城市頂層設計，擔任科技部、北京市科委專家。項目研究成果授權發明專利2項，申請發明專利6項，獲得北京市科學技術獎二等獎、北京市科學技術進步三等獎、冶金科學技術一等獎、冶金礦山科學技術獎特等獎等，出版了《創新理論與實現技術》、《行業信息化知識庫建設實現技術》、《科技與生活同行》、《科學你我他》等系列著作，發表學術論文40余篇。
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