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研究生: 林冠均
Lin, Guan-Jun
論文名稱: 開發自動化讀取與分類文獻資料以探討植物性雌激素對婦女癌症的影響
Developing the Automatical Method to Review and Classify Literatures on the Effect of Phytoestrogens on Women Cancers
指導教授: 謝佳倩
Hsieh, Chia-Chien
學位類別: 碩士
Master
系所名稱: 營養科學碩士學位學程
Graduate Program of Nutrition Science
論文出版年: 2020
畢業學年度: 108
語文別: 中文
論文頁數: 108
中文關鍵詞: 婦女性癌症植物性雌激素自然語言處理分類資料探勘
英文關鍵詞: women cancer, phytoestrogen, Natural Language Processing, classification, text mining
DOI URL: http://doi.org/10.6345/NTNU202001407
論文種類: 學術論文
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  • 乳癌、子宮內膜癌及卵巢癌為婦女性癌症中高發生率的惡性腫瘤,流行病學資料顯示其發生率有逐漸增長的趨勢。婦女性癌症和雌激素的調節與作用息息相關,而經由飲食攝取所含的天然雌激素也可能為影響因子之一,植物性雌激素結構與人體製造的雌二醇相似,可能執行類似或拮抗的功效進而干擾體內雌激素的作用。近年智慧醫療相關產業發達,有越來越多研究是利用電腦資訊技術進行,以協助醫療能夠更加進步、精確與優化。本研究運用電腦自然語言學習工具建立自動化分類程式,對摘要內容以分句方式配合分類關鍵字與訓練資料來進行分類判斷,以幫助文獻資料的分類與整理,減少人力閱讀花費的時間與精力,利用植物雌激素類型、癌症類型、實驗類型這三個類別變項建立資料庫,共包含937篇文獻摘要分為1683筆資料,再使用資料探勘技術及決策樹模型進行資料的分析與統合,接著針對癌症種類如乳癌、子宮內膜癌與卵巢癌,和植物性雌激素類別包含香豆酚 (coumestrol)、大豆苷元 (daidzein)、雌馬酚 (equol)、金雀素黃酮 (genistein)、木酚素 (lignan) 與白藜蘆醇 (resveratrol) 介入研究,以及實驗類型如細胞、動物與人體實驗,進而探討植物雌激素介入後對婦女癌風險發展的相關性。本研究利用卡方檢驗及資料探勘模型如決策樹模型及關聯規則,來分析植物性雌激素介入對婦女性癌症的發展影響。在不同類別植物性雌激素介入,發現植物性雌激素lignan與resveratrol對於婦女性癌症的影響多為正面,而daidzein 與equol造成的負面影響相對較多。針對癌症種類討論,其中乳癌對於genistein、daidzein或equol的使用可能會有較高風險,而卵巢癌在植物性雌激素介入後多是正向反應,而子宮內膜癌則含有較多負面結果,特別是對於daidzein或equol的使用。在實驗類型中,細胞或動物實驗顯示相對較高的負面反應,而人體實驗則為中性結果較多,特別在乳癌的研究。綜合上述,本研究建議乳癌與子宮內膜癌的患者在植物性雌激素的食物攝取上可能會有些微的風險,特別在daidzein或equol的單品使用。

    Breast cancer, endometrial cancer and ovarian cancer are malignant tumors with a high incidence and have high mortality in women. Epidemiological studies show that the incidence of these types of women cancer is increasing nowadays. Breast cancer, endometrial cancer, and ovarian cancer are closely related to the estrogen status. These phytoestrogens have similar structure with the human estradiol, thus, have similar effects and may interfere the action of estrogen in our body. Recently, the smart health industries use computer technologies to develop medical therapy to achieve advanced, accurate and optimized pathway. In this study, a natural language processing system was used to establish an automated classification program to help data classification, reduce the time and effort of the manual reading. We established a database using four classification categories of phytoestrogens, cancers, experiments and research results. The data base was set up contained 937 abstracts including 1683 data. Then we use text mining techniques and decision tree models to analyze the data. We collected three types of women cancer including breast cancer, endometrial cancer, and ovarian cancer, six phytoestrogens, including coumestrol, daidzein, equol, genistein, lignin and resveratrol. The research type was included cell, animal and human studies. We used chi-square and text mining methods, such as decision tree models and association rules, to explore the correlation between phytoestrogens and women cancer development. Regard to the types of phytoestrogens, we found that the phytoestrogens lignin and resveratrol have a higher positive effect on women cancer, while daidzein and equol are more related to negative effects. According to the type of cancer, coumestrol, genistein, daidzein or equol may have more risk on breast cancer, besides there are more positive reactions on ovarian cancer, and more negative results related to endometrial cancer. In the experiment types, cell and animal experiments showed higher negative impacts, while human experiments have more neutral results, especially in breast cancer researches. In conclusion, patients with women cancer should pay more attention to using daidzein and equol. Thus, patients with endometrial or breast cancer may have a slight risk on intake of foods containing coumestrol, daidzein, equol and genistein, especially daidzein and equol.

    第一章 前言 1 一 研究假說 2 二 研究問題 2 第二章 文獻回顧 3 第一節、 婦女性癌症 3 一 乳癌 4 1. 乳癌的風險因子 4 2. 乳癌的介紹與分類 5 3. 乳癌的治療方法 6 4. 乳癌與飲食因子 6 二 卵巢癌 7 1. 卵巢癌的風險因子 8 2. 卵巢癌的介紹與分類 9 3. 卵巢癌的治療方法 10 4. 卵巢癌與飲食因子 10 三、 子宮內膜癌 11 1. 子宮內膜癌的風險因子 11 2. 子宮內膜癌的介紹與分類 12 3. 子宮內膜癌的治療方法 12 4. 子宮內膜癌與飲食因子 13 第二節、 飲食與癌症 13 第三節、 植物雌激素 14 一、 Equol 16 二、 Genistein 16 三、 Daidzein 17 四、 Lignans 17 五、 Coumestrol 18 六、 Resveratrol 18 第四節、 電腦系統生物學 (computational system biology) 22 一、 大數據 (Big data) 23 二、 資料探勘 (Data mining) 24 第三章 研究材料與方法 27 第一節、研究材料來源 27 一、 資料庫資料來源 27 二、 建模與統計分析軟體 27 第二節、 研究方法與模式 28 一、 研究流程與架構 28 二、 資料庫關鍵字筆數分析 29 三、 製作訓練資料 31 四、 自動化分類流程架構 34 五、 分類模型 35 六、 建立本研究資料庫 39 七、 隨機森林 41 八、 關聯規則探勘 42 九、 決策樹 43 十、 卡方檢驗 44 第四章 分析結果 46 第一節、 建立訓練資料進行研究結果之分類 46 第二節、 全部資料庫之分析 47 第三節、 不同類別植物性雌激素介入對婦女性癌症的影響 53 第四節、 不同種類婦女性癌症對於在雌激素介入下的癌症結果影響是否有關 58 第五節、 不同實驗類型對於植物性雌激素對婦女性癌症之關係分析 64 第五章 討論 68 第一節、 建立自動化分類程式分類文獻之摘要 68 第二節、 不同類別植物性雌激素介入對於婦女性癌症的影響 69 第三節、 不同種類婦女性癌症在植物性雌激素介入下對癌症結果的影響 72 第四節、 不同實驗類型對於植物性雌激素對婦女性癌症之關係分析 73 第五節、 本研究之限制 74 第六章 結論 76 參考文獻 78 附錄 92 第一節 卡方檢定 92 第二節 決策樹模型 95 第三節 關聯規則分析 96 第四節 製作研究結果分類模型訓練資料之120篇文獻 99

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