跳到主要內容

臺灣博碩士論文加值系統

(3.235.60.144) 您好!臺灣時間:2021/07/27 00:18
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:江東霖
研究生(外文):CHIANG, TUNG-LIN
論文名稱:植基於隨機森林模型預測移植胚胎的植入潛力之研究
論文名稱(外文):A Study on Predicting Embryo Implantation Based on Random Forest Model
指導教授:高瑞鴻高瑞鴻引用關係
指導教授(外文):KAO, JUI-HUNG
口試委員:廖鴻圖吳翠鳳吳威震
口試委員(外文):LIAW, HORNG-TWUWU, TSUI-FENGWU, WEI-CHEN
口試日期:2021-05-02
學位類別:碩士
校院名稱:世新大學
系所名稱:資訊管理學研究所(含碩專班)
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2021
畢業學年度:109
語文別:中文
論文頁數:62
中文關鍵詞:試管嬰兒血液檢查資料隨機森林不孕症懷孕率
外文關鍵詞:IVFBlood Test DataRandom ForestInfertilityPregnancy Rate
相關次數:
  • 被引用被引用:0
  • 點閱點閱:23
  • 評分評分:
  • 下載下載:9
  • 收藏至我的研究室書目清單書目收藏:0
在試管嬰兒療程(In Vitro Fertilization, IVF)中影響療程的成功與否是有很多因素,與生殖中心醫師溝通及學習後,了解有以下原因,包含:病患(婦女)身體健康、排卵時間選擇、胚胎發育過程、胚胎植入時機、子宮環境因素等。當醫師協助病患做IVF療程時,醫師需要借助許多檢查,包含血液檢驗資料、卵泡超音波檢查資料來改善或治療病患,再利用這些血液檢查資料來提前預測及判斷病患懷孕成功率到底有多大,而醫師需要判別的指標資料有年齡、抗穆勒氏管荷爾蒙(Anti-Mullerian Hormone, AMH)AMH值、黃體刺激素(Luteining Hormone, LH)LH值、黃體激素(Progesterone, P4)P4值、雌二醇(Estradiol, E2)E2值及卵泡超音波測量病患卵巢內的卵泡數量與大小等,醫師再針對各種不同的檢查資料進行研究與分析,而在過程中去了解資料數值的變化,進而能達到提升病患IVF療程的懷孕目的。
經合作之生殖中心醫師指導及說明,上述的檢查資料指標對胚胎品質影響非常高,根據資料統計分析顯示病患年齡、抗穆勒氏管荷爾蒙(AMH)等資料指標在臨床上對病患懷孕機率是有明顯的影響力。所以用這些指標來做預測模型與實際醫師傳統方式的分析資料做比較,進而完成機器深度學習的預測模型,運用在IVF療程懷孕與否的預測,對生殖中心提高植入率是具有重大影響力。
本研究的目的是針對在進行IVF療程的病患,將病患的懷孕率與各項檢測資料的相關性做分析,建立試IVF療程的預測移植胚胎的植入潛力模型。本研究對象共計有1000名有效樣本,將所有研究對象的檢測資料都匯入本研究模型去訓練及驗證後,運用人工智慧演算法做資料的統計分析,這裡會先確立血液檢驗及卵泡超音波測量資料與懷孕率的分析及判斷,並運用這些指標建立隨機森林(Random Forest, RF)演算法訓練模型,並與醫師臨床的判斷對該模型以同樣的樣本做運算後資料比較確認,確認預測模型的可行性,完成對IVF療程之胚胎植入潛力預測的精準性而造福病患。
Many factors affect the success rate of IVF treatment. After studying with the doctor of the reproductive center, I understand the following reasons, including: the patient's health, the choice of ovulation time, the embryonic development process, the timing of embryo implantation, uterine environmental factors, etc.
When doctors assist patients in IVF treatment, doctors need to use many inspection methods, including blood tests, ultrasound and biochemical test data to improve or treat patients, and then use these blood test data to predict and judge the disease in advance.
The index data that doctors need to distinguish include age, AMH value, LH value, P4, E2, and Ultrasonic measures the number and size of follicles in the patient’s ovaries. Doctors then conduct research and analysis on various examination data, and understand the changes in the data values in the process, so as to achieve the purpose of improving the patient's IVF course of pregnancy.
The purpose of this thesis to analyze the pregnancy rate of patients and various test data for patients undergoing infertility treatment, and establish a predictive pregnancy model for IVF treatment. There are 1000 samples in this research object. After the test data of all the research objects are imported into this model for training and verification, the artificial intelligence algorithm is used for statistical analysis of the data. Here, the blood test, ultrasound measurement data, and pregnancy rate are established. Analyze and judge, and use these indicators to establish a random forest algorithm training model, and compare the data to confirm the model with the same sample as the physician’s clinical judgment, confirm the feasibility of the predictive model, and complete the embryo for IVF treatment Implant the accuracy of shallow force prediction.
摘要.....................................................I
Abstract ...............................................II
目錄 ...................................................III
圖目錄..................................................V
表目錄.................................................VI
第一章 緒論..............................................1
1.1研究背景與動機.........................................1
1.2研究目的..............................................3
1.3研究範圍..............................................4
1.4研究程序..............................................5
1.5論文架構..............................................6
第二章 文獻探討...........................................7
2.1試管嬰兒療程步驟.......................................7
2.2抗穆勒氏管荷爾蒙(Anti-Mullerian Hormone, AMH)..........11
2.3濾泡刺激素(Human Follicle-Stimulating Hormone, FSH)....12
2.4血液賀爾蒙檢查.........................................13
2.5卵泡超音波影像檢查......................................15
2.6隨機森林演算法(Random Forest, RF)......................17
2.7支撐向量機(Support Vector Machine, SVM)................21
第三章 研究方法...........................................23
3.1實務檢查流程...........................................23
3.2研究方法...............................................27
3.3資料過濾與採集..........................................31
3.4模型架構設計與預測驗證..................................33
第四章 資料實驗與結果......................................35
4.1實驗環境..............................................35
4.2資料來源及特徵值.......................................38
4.3隨機森林(RF)模型測試結果...............................39
4.4支援向量機(SVM)模型測試結果............................43
4.5驗證之分析............................................46
第五章 結論與未來研究.....................................47
5.1結論.................................................47
5.2未來研究.............................................48
參考文獻................................................50

[1]李欣海,「隨機森林模型在分類與回歸分析中的應用」,應用昆蟲學報,第4期,第1190-1197頁,2019年。
[2]徐翊菁,「基於隨機森林分類之心房顫動發作預測」,臺灣海洋大學電機工程學系碩士論文,2020年。
[3]彰化市博元婦產科院長蔡鋒博,「試管嬰兒想一次成功?要有這4大元素」,2017年。https://health.ettoday.net/news/842550?redirect=1。
[4]董師師、黃哲學,「隨機森林理論淺析」,集成技術2013年01期 ,2013年。
[5]衛生福利部國民健康署,「107年人工生殖施行結果分析報告」,2020年。
[6]A. I. Csapo, M. O. Pulkkinen, & W. Wiest, “Effects of luteectomy and progesterone replacement therapy in early pregnant patients,” American Journal of Obstetrics and Gynecology, vol. 115, no. 6, pp. 759-765, 2018.
[7]A. Iwase, S. Osuka, M. Goto, T. Murase, T. Nakamura, S. Takikawa, & F. Kikkawa, “Clinical application of serum anti‐Müllerian hormone as an ovarian reserve marker: A review of recent studies,” Journal of Obstetrics and Gynaecology Research, vol. 44, no. 6, pp. 998-1006, 2018.
[8]A. Kamel, A. A. Halim, M. Shehata, S. AlFarra, Y. El-Faissal, W. Ramadan, & A. M. Hussein, “Changes in serum prolactin level during intracytoplasmic sperm injection, and effect on clinical pregnancy rate: a prospective observational study,” BMC Pregnancy and Childbirth, vol. 18, no. 1, pp. 141-142, 2018.
[9]Apache Spark, “Classification and regression - Spark 2.2.0 documentation,” https://spark.apache.org/docs/2.2.0/ml-classification-regression.html, 2021.
[10]Barragán M, Pons J, Ferrer-Vaquer A, Cornet-Bartolomé D, Schweitzer A, Hubbard J, Auer H, Rodolosse A, & Vassena R. T., “The transcriptome of human oocytes is related to age and ovarian reserve,” Mol Hum Reprod, 2017.
[11]Ben-Hur, Asa, Horn, David, Siegelmann, Hava, & Vapnik, Vladimir; “Support vector clustering,” Journal of Machine Learning Research, vol. 2, no. 2, pp. 125-137, 2001.
[12]C. Blank, R. R. Wildeboer, I. DeCroo, K. Tilleman, B. Weyers, P. De Sutter, M. Mischi, & B. C. Schoot, “Prediction of implantation after blastocyst transfer in in vitro fertilization: a machine-learning perspective,” Fertility and Sterility, vol. 111, no. 2, pp. 318-326, 2019.
[13]C. Gnoth, E. Godehardt, P. Frank-Herrmann, K. Friol, J. Tigges, & G. Freundl, “Definition and prevalence of subfertility and infertility,” Human Reproduction, vol. 20, no. 5, pp. 1144-1147, 2017.
[14]Cortes, C. & Vapnik, V., “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273-297, doi:10.1007/BF00994018, 1995.
[15]C. Sonigo, I. Beau, N. Binart, and M. Grynberg, “Anti-Müllerian hormone in fertility preservation: Clinical and therapeutic applications,” Clinical Medicine Insights: Reproductive Health, vol. 13, no. 1, pp. 1-7, 2019.
[16]D. Santi, P. Crépieux, E. Reiter, G. Spaggiari, G. Brigante, L. Casarini, V. Rochira, & M. Simoni, “Follicle-Stimulating Hormone (FSH) action on spermatogenesis: A focus on physiological and therapeutic roles,” JJ. Clin. Med, vol. 9, no. 4; doi:10.3390/jcm9041014, 2020.
[17]D. Zhang, X. Yuan, J. Zhen, Z. Sun, C. Deng, & Q. Yu, “Mildly higher serum prolactin levels Are directly proportional to cumulative pregnancy outcomes in in-vitro fertilization/intracytoplasmic sperm injection cycles,” Frontiers in Endocrinology, Frontiers in Endocrinology, https : // doi.org / 10.3389 / fendo. 2020.00584, 2020.
[18]E. H. Yu Ng, W. S. B. Yeung, E. Yee Lan Lau, W. W. K. So, & P. C. Ho, “High serum oestradiol concentrations in fresh IVF cycles do not impair implantation and pregnancy rates in subsequent frozen–thawed embryo transfer cycles,” Human Reproduction, vol. 15, no. 2, pp. 250-255, 2020.
[19]E. T. Vestergaard, M. E. Schjørring, K. Kamperis, K. K. Petersen, S. Rittig, A. Juul, K. Kristensen, & N. H. Birkebæk, “The follicle-stimulating hormone (FSH) and luteinizing hormone (LH) response to a gonadotropin-releasing hormone analogue test in healthy prepubertal girls aged 10 months to 6 years,” European Journal of Endocrinology, vol. 176, no. 6, pp. 747-753, doi: 10.1530/EJE-17-0042. Epub, 2017.
[20]F. Zegers-Hochschild, G. D. Adamson, J. de Mouzon, O. Ishihara, R. Mansour, K. Nygren, E. Sullivan, & S. Van der Poel, “The international committee for monitoring assisted reproductive technology (ICMART) and the world health organization (WHO) revised glossary on ART terminology,” Human Reproduction, vol. 24, no. 11, pp. 2683-2687, 2019.
[21]Gurtcheff SE, & Klein NA, “Diminished ovarian reserve and infertility,” Clin Obstet Gynecol, 2019.
[22]Heinrichs C, Bourdoux P, Saussez C, Vis HL, & Bourguignon JP., “Blood spot follicle-stimulating hormone during early postnatal life in normal girls and Turner’s syndrome,” Journal of Clin Endocrinol Metab, vol. 78, no. 4, pp. 978-981, 2017.
[23]K. Neumann, M. Depenbusch, A. Schultze-Mosgau, & G. Griesinger, “Strong variation in progesterone production of the placenta in early pregnancy–what are the clinical implications?,” Reproductive BioMedicine Online, vol. 41, no. 4, pp. 748-749, 2020.
[24]L. B. Håkonsen, A. M. Thulstrup, A. S. Aggerholm, J. Olsen, J. P. Bonde, C. Y. Andersen, M. Bungum, E. H. Ernst, M. L. Hansen, & E. H. Ernst, “Does weight loss improve semen quality and reproductive hormones? Results from a cohort of severely obese men,” Reproductive Health, vol. 8, no. 1, pp. 2-3, 2019.
[25]N. Chen, L. Luo, C. Zhang, J. Liu, W. Wang, Y. Li, J. Zhu, D. Wang, L. Zeng, & H. Huang, “Anti-Müllerian hormone participates in ovarian granulosa cell damage due to cadmium exposure by negatively regulating stem cell factor,” Reproductive Toxicology, vol. 93, no. 1, pp. 54-60, 2020.
[26]N. Josso, “Women in reproductive science: anti-müllerian hormone: a look back and ahead,” Reproduction, vol. 158, no. 6, pp. F81-F89, 2019.
[27]P. Lancaster, & J. de Mouzon, “Global committee reproductive ART surveillance: monitoring technologies assisted the (ICMART) international,” Assisted Reproductive Technology Surveillance, pp. 101-105, 2019.
[28]S. Gurunath, Z. Pandian, R. A. Anderson, & S. Bhattacharya, “Defining infertility—a systematic review of prevalence studies,” Human Reproduction Update, vol. 17, no. 5, pp. 575-588, 2018.
[29]S. R. Oh, S. Y. Choe, & Y. J. Cho, “Clinical application of serum anti-Müllerian hormone in women,” Clinical and Experimental Reproductive Medicine, vol. 46, no. 2, pp. 50-59, 2019.
[30]Tal R, Seifer DB., “Ovarian reserve testing: a user’s guide,” Journal of Obstet Gynecol, vol. 217, pp.129-140, 2017.
[31]T. K. Ho, “Random decision forests,” Proceedings of 3rd International Conference on Document Analysis and Recognition, vol. 1, pp. 278-282, Aug. 14-16, 2019.
[32]William H., Teukolsky, Saul A. Vetterling, William T., & Flannery, B. P., “Support vector machines. numerical recipes: the art of scientific computing 3rd,” New York: Cambridge University Press, 2018.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top