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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 周承復(Cheng-Fu Chou) | |
| dc.contributor.author | Hung-Chi Hsieh | en |
| dc.contributor.author | 謝宏祺 | zh_TW |
| dc.date.accessioned | 2022-11-23T09:14:31Z | - |
| dc.date.available | 2021-11-06 | |
| dc.date.available | 2022-11-23T09:14:31Z | - |
| dc.date.copyright | 2021-11-06 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-10-08 | |
| dc.identifier.citation | JaDer Liang, XiaoOu Ping, YiJu Tseng, GuanTarn Huang, Feipei Lai, and PeiMing Yang. Recurrence predictive models for patients with hepatocellular carcinoma after radiofrequency ablation using support vector machines with feature selectionmethods. Computer Methods and Programs in Biomedicine, 117(3):425–434, 2014. PoWen Chen. Recurrence predictive models for patients with hepatocellular carcinoma after radiofrequency ablation based on machine learning algorithm. Master’s thesis, National Taiwan University, Taipei, August 2019. Rebecca L Siegel, Kimberly D Miller, and Ahmedin Jemal. Cancer statistics, 2020. CA Cancer J Clin., 70(1):7–30, 2020. F Xavier Bosch, Josepa Ribes, Mireia Díaz, and Ramon Cléries. Primary liver cancer: Worldwide incidence and trends. Gastroenterology, 127(5):S5–S16, 2004. Isabella Lurje, Zoltan Czigany, Jan Bednarsch, Christoph Roderbur, Peter Isfort,Ulf Peter Neumann, and Georg Lurje. Treatment strategies for hepatocellular carcinoma a multidisciplinary approach. Int J Mol Sci., 20(6):1465, 2019. Yasunori Minami, Naoshi Nishida, and Masatoshi Kudo. Therapeutic response assessment of rfa for hcc: contrast-enhanced us, ct and mri. World J Gastroenterol.,20(15):4160–4166, 2014. F Xavier Bosch, Josepa Ribes, Mireia Díaz, and Ramon Cléries. Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence,27:1226–1238, 2005. Vincent WaiTo Lam, Kelvin KwokChai Ng, Kenneth SiuHo Chok, TanTo Cheung, Jimmy Yuen, Helen Tung, WaiKuen Tso, SheungTat Fan, and Ronnie T PPoon. Risk factors and prognostic factors of local recurrence after radiofrequency ablation of hepatocellular carcinoma. J Am Coll Surg., 207(1):20–29, 2008. Kazue Shiozawa, Manabu Watanabe, Noritaka Wakui, Takashi Ikehara, KazunariIida, and Yasukiyo Sumino. Risk factors for the local recurrence of hepatocellular carcinoma after single-session percutaneous radiofrequency ablation with a single electrode insertion. Mol Med Rep., 2(1):89–95, 2009. LunXiu Qin and ZhaoYou Tang. The prognostic significance of clinical and pathological features in hepatocellular carcinoma. World J Gastroenterol., 8(2):193–199,2002. Jeong Han Kim, Hyung Joon Yim, Kwang Gyun Lee, Seung Young Kim, Eun SukJung, Young Kul Jung, Ji Hoon Kim, Yeon Seok Seo, Jong Eun Yeon, Hong SikLee, Soon Ho Um, Kwan Soo Byun, and Ho Sang Ryu. Recurrence rates and factors for recurrence after radiofrequency ablation combined with transarterial chemoembolization for hepatocellular carcinoma: a retrospective cohort study. Hepatol Int.,6(2):505–510, 2002. Kazuhiro Nouso, Eiji Matsumoto, Yoshiyuki Kobayashi, ShinIchiro Nakamura, Hironori Tanaka, Toshiya Osawa, Hiroshi Ikeda, Yasuyuki Araki, Kohsaku Sakaguchi, and Yasushi Shiratori. Risk factors for local and distant recurrence of hepatocellular carcinomas after local ablation therapies. J Gastroenterol Hepatol., 23(3):453–458,2008. Andrew L. Maas, Awni Y. Hannun, and Andrew Y. Ng. Rectifier nonlinearities improve neural network acoustic models. In ICML, 2013. Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, and Ilya Sutskever. Dropout:A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15:1929–1958, 2014. F Xavier Bosch, Josepa Ribes, Mireia Díaz, and Ramon Cléries. Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research, 12:2121–2159, 2011. Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, 2015. Barret Zoph and Quoc V. Le. Neural architecture search with reinforcement learning. In ICLR, 2017. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/79867 | - |
| dc.description.abstract | 肝細胞癌在各種癌症中的死亡率常年位居前列，即使病患診斷出肝癌腫瘤並且接受治療，術後仍有很高的機率復發。因此，透過病患術前的檢體採檢資料以及各項影像報告的整合分析，輔以術後的檢體採檢做為追蹤，建立復發的預測模型，可以提早的發現腫瘤的復發或者轉移並且及時治療。 本篇論文使用的資料是於2007-2019年間，以射頻灼燒術作為第一次肝癌治療的病患。資料樣本總數為1477筆，其中362筆術後一年內復發的病患，562筆為術後一年以上復發，其餘553筆為一年以上未復發。這份資料集取自台大醫院資料庫，先前有團隊研究了2007-2009以及2007-2013年間的資料，並且發表數篇研究成果，包括建立資料庫、特徵提取以及資料缺值插補等主題。其中也包含復發預測模型的建立，但僅使用支援向量機與簡單深度神經網路，且特徵也僅包含術前資料，並無術後的追蹤。 本篇論文聚焦於整合文字報告與檢體採檢同時的異質輸入、術後追蹤的時序關係以及使用搜索的方式找出這些特徵的線性組合來擴增特徵，同時與之前團隊之研究成果進行比較。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-23T09:14:31Z (GMT). No. of bitstreams: 1 U0001-0208202101590400.pdf: 1414297 bytes, checksum: 5733a18bfb0eef86d59e19ec99083392 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | Verification Letter from the Oral Examination Committee i 致謝 iii 摘要 v Abstract vii Contents ix List of Figures xi List of Tables xiii Chapter 1 Introduction 1 Chapter 2 Related Work 3 2.1 Recurrence Predictive Model using SVM. . . . . . . . . . . . . . . 3 2.2 Predictive Model based on machine learning algorithm. . . . . . . . 4 2.3 FeatureWiz. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.3.1 SULOV method. . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.3.2 Recursive XGBoost. . . . . . . . . . . . . . . . . . . . . . . . . . 6 Chapter 3Dataset9 3.1 Overview. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.2 Freetext report. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.3 laboratory inspection data from Department of Laboratory Medicine. 11 Chapter 4 Method 13 4.1 Data preprocessing. . . . . . . . . . . . . . . . . . . . . . . . . . . 13 4.1.1 Freetext reports. . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 4.1.2 laboratory inspection data. . . . . . . . . . . . . . . . . . . . . . . 14 4.2 Base method. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 4.2.1 Model description. . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4.2.2 Hyperparameter. . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.3 Method based on feature selection. . . . . . . . . . . . . . . . . . . 23 4.3.1 Overview. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 4.3.2 Search Space. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.3.3 Controller. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.3.4 Training with Reinforcement. . . . . . . . . . . . . . . . . . . . . 25 Chapter 5 Experiments and Results 27 5.1 Evaluation metrics. . . . . . . . . . . . . . . . . . . . . . . . . . . 27 5.2 Experiments and Results. . . . . . . . . . . . . . . . . . . . . . . . 29 5.2.1 Simple test. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 5.2.2 Kfold Cross validation. . . . . . . . . . . . . . . . . . . . . . . . 31 5.2.3 Threeyear recurrence prediction. . . . . . . . . . . . . . . . . . . 33 Chapter 6 Conclusion 35 References 37 | |
| dc.language.iso | en | |
| dc.subject | 異質輸入 | zh_TW |
| dc.subject | 肝癌 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 特徵搜索 | zh_TW |
| dc.subject | Hepatocellular carcinoma | en |
| dc.subject | Heterogeneous input | en |
| dc.subject | Feature searching | en |
| dc.subject | Machine learning | en |
| dc.title | 基於特徵搜索與異質輸入的射頻灼燒術後肝癌復發預測模型 | zh_TW |
| dc.title | Recurrence Predictive Models for Patients with Hepatocellular Carcinoma after Radiofrequency Ablation based on Feature Searching and Heterogeneous Input | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 吳曉光(Hsin-Tsai Liu),林澤(Chih-Yang Tseng),梁嘉德,李明穗 | |
| dc.subject.keyword | 肝癌,異質輸入,特徵搜索,機器學習, | zh_TW |
| dc.subject.keyword | Hepatocellular carcinoma,Heterogeneous input,Feature searching,Machine learning, | en |
| dc.relation.page | 39 | |
| dc.identifier.doi | 10.6342/NTU202101983 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2021-10-12 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
| 顯示於系所單位： | 資訊網路與多媒體研究所 | |
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| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| U0001-0208202101590400.pdf | 1.38 MB | Adobe PDF | 檢視/開啟 |
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