中藥單方的適應症與其於中藥複方中的重要性之間的關係

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姓名

黄玉(Huynh Ngoc Quynh Nhu) 查詢紙本館藏

畢業系所

生醫科學與工程學系

論文名稱

中藥單方的適應症與其於中藥複方中的重要性之間的關係
(Relationship between Al predictions and roles in classical formulas of individual TCM herbs)

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摘要(中)

人工智慧（或深度學習）自其復興和成熟以來，改變了許多生物醫學研究，甚至在五年前（2016至今）對相關研究的發展更加迅速。我們提議將應用在西藥的最先進深度學習技術擴展至中藥領域。因此，我們開發了卷積神經網絡（CNN）將中藥處方分類為對應的疾病。經過CNN訓練後，使用了來自國家衛生局保險數據庫中的報銷數據，CNN輸出以下內容的概率（稱為AI分數）對應給定輸入中草藥治療的疾病。為了更好地理解CNN的輸出以及中藥從多種草藥中合成配方的方式，我們從數百種中藥配方裡計算每個藥草的平均比例權重（稱為重要性分數）。結果顯示，AI分數和重要性分數之間的相關性表明中藥配方裡的藥草組合公式並非簡單加法（即線性）。

摘要(英)

Artificial intelligence (or deep learning) has accelerated and even transformed many of the biomedical research since its resurgence and maturity five years ago (around 2016). We propose to extend applications of state-of-the-art deep learning techniques to traditional Chinese medicine (TCM) from modern western medicine. In doing so, we developed a convolutional neural network (CNN) to classify TCM herbal prescriptions into their corresponding diseases. After CNN training using reimbursement data from the National Health Insurance Database, the CNN outputs probabilities (called AI scores) of the indicated diseases given an input TCM herb. To make better sense of the CNN outputs and the way TCM composes formulas from multiple herbs, we then calculated the average proportional weight (called importance score) of an individual herb in hundreds of TCM formulas found in TCM classics. The result of correlation between AI scores and importance scores indicates that the functions of TCM formulas are not simple linear addition of individual herbs.

關鍵字(中)

★ 人工智慧
★ 卷積神經網絡
★ 中藥單方
★ 中藥複方

關鍵字(英)

★ artificial intelligence
★ CNN
★ Chinese single herb
★ Chinese medicine formula

論文目次

中文摘要 I
ENGLISH ABSTRACT II
ACKNOWLEDGEMENT III
CONTENTS IV
LIST OF FIGURES VI
LIST OF TABLES VI
CHAPTER 1 INTRODUCTION 1
1.1 TRADITIONAL CHINESE MEDICINE (TCM) 1
1.1.1 The philosophy of well-being 1
1.1.2 The philosophy of disease 4
1.1.3 Chinese herbal medicine 5
1.1.3.1 – DIAPHORETICS 6
1.1.3.2 – HEAT CLEARING 6
1.1.3.3 – LAXATIVE 7
1.1.3.4 – ANTI-RHEUMATICS 7
1.1.3.5 – EXPECTORANT 8
1.1.3.6 – DIGESTIVE SUPPORT 8
1.1.3.7 – Qi REGULATION 8
1.1.3.8 – BLOOD REGULATION 8
1.1.3.9 – TONICS 8
1.1.3.10 – ASTRINGENTS 9
1.1.3.11 – TRANQUILIZING 10
1.1.3.12 – ANTHELMINTICS (ANTI-HELMINTICS) 10
1.1.3.13 – INTERIOR WARMING 10
1.1.3.14 – INTERNAL WIND EXTINGUISHMENT 10
1.1.3.15 – DIURETICS 11
1.1.4. The philosophy of an herbal prescription 11
1.2 CONCENTRATED HERBAL EXTRACT GRANULE (CHEG) 12
1.3 ARTIFICIAL INTELLIGENCE (AI) 14
1.3.1 – SUPERVISED LEARNING 16
1.3.2 – NEURAL NETWORKS 18
1.3.3– DEEP LEARNING 21
1.3.4– CONVOLUTIONAL NEURAL NETWORKS (CNN) 23
1.4 ANALYZING TRADITIONAL CHINESE MEDICINE (TCM) WITH ARTIFICIAL INTELLIGENCE (AI) 25
CHAPTER 2 MATERIAL AND METHODS 26
2.1 MATERIAL 26
2.1.1: AI model mastering CHEG prescriptions (Al score) 26
2.1.2: Individual herb score (IH score) 28
2.2 METHOD 29
CHAPTER 3 RESULTS 32
3.1 HISTOGRAM 32
3.2 PEARSON CORRELATION COEFFICIENT 34
CHAPTER 4 DISCUSSION & CONCLUSION 38
REFERENCE 41
SUPPLEMENTARY TABLE 44

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指導教授

王孫崇(SUN CHONG WANG)

審核日期

2021-1-25

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