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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳倩瑜 | zh_TW |
| dc.contributor.advisor | Chien-Yu Chen | en |
| dc.contributor.author | 張名翔 | zh_TW |
| dc.contributor.author | Ming-Siang Chang | en |
| dc.date.accessioned | 2023-09-07T16:27:14Z | - |
| dc.date.available | 2025-01-07 | - |
| dc.date.copyright | 2023-09-11 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-08-09 | - |
| dc.identifier.citation | Albawi, S., Mohammed, T. A., & Al-Zawi, S. (2017). Understanding of a convolutional neural network. 2017 international conference on engineering and technology (ICET),
Alshdaifat, E. a., Alshdaifat, D. a., Alsarhan, A., Hussein, F., & El-Salhi, S. M. d. F. S. (2021). The Effect of Preprocessing Techniques, Applied to Numeric Features, on Classification Algorithms’ Performance. Data, 6(2), 11. https://www.mdpi.com/2306-5729/6/2/11 Bergstra, J., Yamins, D., & Cox, D. D. (2013). Hyperopt: A python library for optimizing the hyperparameters of machine learning algorithms. Proceedings of the 12th Python in science conference, Breiman, L. (2001). Random forests. Machine learning, 45, 5-32. Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, Döhner, H., Estey, E., Grimwade, D., Amadori, S., Appelbaum, F. R., Büchner, T., Dombret, H., Ebert, B. L., Fenaux, P., & Larson, R. A. (2017). Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel. Blood, The Journal of the American Society of Hematology, 129(4), 424-447. Döhner, H., Wei, A. H., Appelbaum, F. R., Craddock, C., DiNardo, C. D., Dombret, H., Ebert, B. L., Fenaux, P., Godley, L. A., & Hasserjian, R. P. (2022). Diagnosis and management of AML in adults: 2022 recommendations from an international expert panel on behalf of the ELN. Blood, The Journal of the American Society of Hematology, 140(12), 1345-1377. Hosmer Jr, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (Vol. 398). John Wiley & Sons. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 30. Kim, T. K. (2015). T test as a parametric statistic. Korean journal of anesthesiology, 68(6), 540-546. Noble, W. S. (2006). What is a support vector machine? Nature biotechnology, 24(12), 1565-1567. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., & Dubourg, V. (2011). Scikit-learn: Machine learning in Python. the Journal of machine Learning research, 12, 2825-2830. Peterson, L. E. (2009). K-nearest neighbor. Scholarpedia, 4(2), 1883. Rubinstein, R. (1999). The cross-entropy method for combinatorial and continuous optimization. Methodology and computing in applied probability, 1, 127-190. Shreve, J., Meggendorfer, M., Awada, H., Mukherjee, S., Walter, W., Hutter, S., Makhoul, A., Hilton, C. B., Radakovich, N., & Nagata, Y. (2019). A personalized prediction model to risk stratify patients with acute myeloid leukemia (AML) using artificial intelligence. Blood, 134, 2091. Tien, F.-M., Hou, H.-A., Tang, J.-L., Kuo, Y.-Y., Chen, C.-Y., Tsai, C.-H., Yao, M., Lin, C.-T., Li, C.-C., & Huang, S.-Y. (2018). Concomitant WT1 mutations predict poor prognosis in acute myeloid leukemia patients with double mutant CEBPA. Haematologica, 103(11), e510. Tsai, C.-H. (2021). Applying Next-generation Sequencing to Explore the Risk Stratification in Acute Myeloid Leukemia Patients Upton, G. J. (1992). Fisher's exact test. Journal of the Royal Statistical Society: Series A (Statistics in Society), 155(3), 395-402. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/89305 | - |
| dc.description.abstract | 急性骨髓性白血病（Acute myeloid leukemia, AML）是一種致命的血液疾病，由異常白細胞引起並在骨髓中發展。它會導致血小板減少，增加出血和感染的可能性。本論文開發了一個機器學習集成（ensemble）模型，使用國立台灣大學附設醫院 1213 名 AML患者的數據集，對AML風險進行分層，本研究提出的方法結合機器學習集成模型預測的結果和2017年歐洲白血病網（European LeukemiaNet 2017, ELN 2017）預測的結果，進一步合成最終的集成模型Ensemble (ML+ELN)，提出了初步的臨床風險分層建議。與ELN 2017臨床診斷建議相比，本研究的風險分層建議提供了最佳區分各種風險的能力，c-index 由0.64提升至0.66。特別在區分不利風險和中等風險上，相較於2017 ELN的p值（p-value）平均0.13，本研究的風險分層建議達到p值平均0.001的表現。 | zh_TW |
| dc.description.abstract | Acute myeloid leukemia (AML), a fatal blood condition, is brought on by abnormal white blood cells and develops in the bone marrow. It results in a decrease in platelets, raising the possibility of bleeding and infection. This study developed an ML-based ensemble model to stratify the risk of AML using a dataset containing 1213 AML patients from the National Taiwan University Hospital. Combining the ML-based ensemble model predictions and the European LeukemiaNet (ELN) 2017 predictions, the study represents a final ensemble model (ML+ELN) for initial clinical risk stratification recommendations. Compared to the clinical diagnostic recommendations ELN 2017, the proposed risk stratification proposal provides a superior capacity to distinguish various risks and improve the c-index from 0.64 to 0.66. Especially in distinguishing unfavorable risks from moderate risks, compared with the average p-value (p-value) of 0.13 in 2017 ELN, the proposed risk stratification proposal achieves excellent performance with an average p-value of 0.001. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-09-07T16:27:13Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-09-07T16:27:14Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Acknowledgments i
摘要 ii Abstract iii Table of Contents iv List of Figures vi List of Tables vii Chapter 1. Introduction 1 1.1 Risk Stratification 1 1.2 Biomarkers 2 1.3 Purpose 2 Chapter 2. Literature Review 4 2.1 Clinician vs. Non-Clinician Data: Implications for Health ML Models 4 2.2 Risk Stratification 5 2.3 Biomarkers Interaction 6 2.4 Ensemble 6 2.5 Evaluation Methods 6 Chapter 3. Materials and Methods 11 3.1 Dataset 11 3.2 Models 13 3.3 Hyperparameters Optimization 18 3.4 Machine Learning-based Ensemble Model (Ensemble ML) 18 3.5 Clinical Risk Stratification Recommendations by the Combination of Ensemble Model and ELN 2017 (Ensemble ML+ELN) 19 Chapter 4. Results and Discussion 21 4.1 Performance 22 4.2 Revealing Biomarker Interactions to Distinguish the Adverse and Intermediate of Survival Curves 27 4.3 Insights for patients predicted as adverse on ELN 2017 27 4.4 Insights for patients predicted as favorable on ELN 2017 29 4.5 Identical Risk Prediction on ELN 2017 and the Ensemble Model 31 Chapter 5. Conclusions 33 References 34 Appendices 36 | - |
| dc.language.iso | en | - |
| dc.title | 結合機器學習與臨床指引之急性骨髓性白血病風險分層集成模型 | zh_TW |
| dc.title | An Ensemble Model for Acute Myeloid Leukemia Risk Stratification Recommendations by Combining Machine Learning with Clinical Guidelines | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 蔡承宏;蔡懷寬 | zh_TW |
| dc.contributor.oralexamcommittee | Cheng-Hong Tsai;Huai-Kuang Tsai | en |
| dc.subject.keyword | 急性骨髓性白血病,集成模型,機器學習,風險分層,歐洲白血病網, | zh_TW |
| dc.subject.keyword | acute myeloid leukemia,ensemble model,machine learning,risk stratification,European LeukemiaNet, | en |
| dc.relation.page | 37 | - |
| dc.identifier.doi | 10.6342/NTU202303243 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2023-08-10 | - |
| dc.contributor.author-college | 生物資源暨農學院 | - |
| dc.contributor.author-dept | 生物機電工程學系 | - |
| dc.date.embargo-lift | 2025-01-07 | - |
| 顯示於系所單位： | 生物機電工程學系 | |
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