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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 周呈霙(Cheng-Ying Chou) | |
dc.contributor.advisor | 周呈霙(Cheng-Ying Chou | chengying@ntu.edu.tw | ), | |
dc.contributor.author | Cheng-Yan Wu | en |
dc.contributor.author | 吳承彥 | zh_TW |
dc.date.accessioned | 2023-03-19T23:40:56Z | - |
dc.date.copyright | 2022-09-06 | |
dc.date.issued | 2022 | |
dc.date.submitted | 2022-09-05 | |
dc.identifier.citation | [1] Pierangela Bruno and Francesco Calimeri. Using heatmaps for deep learning based disease classification. In 2019 IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), pages 1–7. IEEE, 2019. [2] Paul E Marik and Abdalsamih M Taeb. Sirs, qsofa and new sepsis definition. Journal of Thoracic Disease, 9(4):943, 2017. [3] Eamon P Raith, Andrew A Udy, Michael Bailey, Steven McGloughlin, Christopher MacIsaac, Rinaldo Bellomo, David V Pilcher, et al. Prognostic accuracy of the sofa score, sirs criteria, and qsofa score for in-hospital mortality among adults with suspected infection admitted to the intensive care unit. JAMA, 317(3):290–300, 2017. [4] Andrew Lever and Iain Mackenzie. Sepsis: definition, epidemiology, and diagnosis. BMJ, 335(7625):879–883, 2007. [5] Elizabeth K Stevenson, Amanda R Rubenstein, Gregory T Radin, Renda Soylemez Wiener, and Allan J Walkey. Two decades of mortality trends among patients with severe sepsis: a comparative meta-analysis. Critical Care Medicine, 42(3):625, 2014. [6] Greg S Martin. Sepsis, severe sepsis and septic shock: changes in incidence,pathogens and outcomes. Expert Review of Anti-Infective Therapy, 10(6):701–706,2012. 49 [7] Bodin Khwannimit, Rungsun Bhurayanontachai, and Veerapong Vattanavanit. Comparison of the performance of sofa, qsofa and sirs for predicting mortality and organ failure among sepsis patients admitted to the intensive care unit in a middle-income country. Journal of Critical Care, 44:156–160, 2018. [8] Omar A Usman, Asad A Usman, and Michael A Ward. Comparison of sirs, qsofa, and news for the early identification of sepsis in the emergency department. TheAmerican Journal of Emergency Medicine, 37(8):1490–1497, 2019. [9] Jesus E Pino, Fergie J Ramos Tuarez, Jorge E Saona, Kai Chen, Endri Ceka, Julio Grajeda Chavez, Andres Chacon Martinez, Charles Bornmann, Pedro Torres, and Robert Chait. Misdiagnosis of sepsis in patients with acutely decompensated heart failure. real world outcomes. Journal of Cardiac Failure, 25(8):S150, 2019. [10] Åsa Askim, Florentin Moser, Lise T Gustad, Helga Stene, Maren Gundersen, Bjørn Olav Åsvold, Jostein Dale, Lars Petter Bjørnsen, Jan Kristian Damås, and Erik Solligård. Poor performance of quick-sofa (qsofa) score in predicting severe sepsis and mortality–a prospective study of patients admitted with infection to the emergency department. Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine, 25(1):1–9, 2017. [11] Jean-Louis Vincent. The clinical challenge of sepsis identification and monitoring. PLoS Medicine, 13(5):e1002022, 2016. [12] Dongdong Zhang, Changchang Yin, Katherine M Hunold, Xiaoqian Jiang, Jeffrey M Caterino, and Ping Zhang. An interpretable deep-learning model for early prediction of sepsis in the emergency department. Patterns, 2(2):100196, 2021. 50 [13] Chen Lin, Yuan Zhang, Julie Ivy, Muge Capan, Ryan Arnold, Jeanne M Huddleston, and Min Chi. Early diagnosis and prediction of sepsis shock by combining static and dynamic information using convolutional-lstm. In 2018 IEEE International Conference on Healthcare Informatics (ICHI), pages 219–228. IEEE, 2018. [14] Hoyt Burdick, Eduardo Pino, Denise Gabel-Comeau, Carol Gu, Jonathan Roberts, Sidney Le, Joseph Slote, Nicholas Saber, Emily Pellegrini, Abigail Green-Saxena, et al. Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 us hospitals. BMC Medical Informatics and Decision Making, 20(1):1–10, 2020. [15] Joseph Futoma, Sanjay Hariharan, Katherine Heller, Mark Sendak, Nathan Brajer, Meredith Clement, Armando Bedoya, and Cara O'brien. An improved multi-output gaussian process rnn with real-time validation for early sepsis detection. In Machine Learning for Healthcare Conference, pages 243–254. PMLR, 2017. [16] Supreeth P Shashikumar, Qiao Li, Gari D Clifford, and Shamim Nemati. Multiscale network representation of physiological time series for early prediction of sepsis. Physiological Measurement, 38(12):2235, 2017. [17] Joseph Guillén, Jiankun Liu, Margaret Furr, Tianyao Wang, Stephen Strong, Christopher C Moore, Abigail Flower, and Laura E Barnes. Predictive models for severe sepsis in adult icu patients. In 2015 Systems and Information Engineering Design Symposium, pages 182–187. IEEE, 2015. [18] Michael Moor, Max Horn, Bastian Rieck, Damian Roqueiro, and Karsten Borgwardt. Early recognition of sepsis with gaussian process temporal convolutional networks 51 and dynamic time warping. In Machine Learning for Healthcare Conference, pages 2–26. PMLR, 2019. [19] Thomas Desautels, Jacob Calvert, Jana Hoffman, Melissa Jay, Yaniv Kerem, Lisa Shieh, David Shimabukuro, Uli Chettipally, Mitchell D Feldman, Chris Barton, et al. Prediction of sepsis in the intensive care unit with minimal electronic health record data: a machine learning approach. JMIR Medical Informatics, 4(3):e5909, 2016. [20] Shamim Nemati, Andre Holder, Fereshteh Razmi, Matthew D Stanley, Gari D Clifford, and Timothy G Buchman. An interpretable machine learning model for accurate prediction of sepsis in the icu. Critical Care Medicine, 46(4):547, 2018. [21] Christopher Barton, Uli Chettipally, Yifan Zhou, Zirui Jiang, Anna Lynn-Palevsky, Sidney Le, Jacob Calvert, and Ritankar Das. Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs. Computers in biology and medicine, 109:79–84, 2019. [22] Matthieu Scherpf, Felix Gräßer, Hagen Malberg, and Sebastian Zaunseder. Predicting sepsis with a recurrent neural network using the mimic iii database. Computers in Biology and Medicine, 113:103395, 2019. [23] Lucas M Fleuren, Thomas LT Klausch, Charlotte L Zwager, Linda J Schoonmade, Tingjie Guo, Luca F Roggeveen, Eleonora L Swart, Armand RJ Girbes, Patrick Thoral, Ari Ercole, et al. Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy. Intensive Care Medicine, 46(3):383–400, 2020. [24] Mitchell M Levy, Mitchell P Fink, John C Marshall, Edward Abraham, Derek Angus, Deborah Cook, Jonathan Cohen, Steven M Opal, Jean-Louis Vincent, and Graham 52 Ramsay. 2001 sccm/esicm/accp/ats/sis international sepsis definitions conference. Intensive Care Medicine, 29(4):530–538, 2003. [25] Christopher W Seymour, Vincent X Liu, Theodore J Iwashyna, Frank M Brunkhorst, Thomas D Rea, André Scherag, Gordon Rubenfeld, Jeremy M Kahn, Manu ShankarHari, Mervyn Singer, et al. Assessment of clinical criteria for sepsis: for the third international consensus definitions for sepsis and septic shock (sepsis-3). JAMA, 315(8):762–774, 2016. [26] Alistair EW Johnson, Tom J Pollard, Lu Shen, Li-wei H Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark. Mimic-iii, a freely accessible critical care database. Scientific Data, 3(1):1–9, 2016. [27] Alistair Johnson, Lucas Bulgarelli, Tom Pollard, Steven Horng, Leo Anthony Celi, and R Mark IV. Mimic-iv (version 0.4). PhysioNet, 2020. [28] M Michael Shabot. The hp carevue clinical information system. International Journal of Clinical Monitoring and Computing, 14(3):177–184, 1997. [29] Alistair E W Johnson, David J Stone, Leo A Celi, and Tom J Pollard. The mimic code repository: enabling reproducibility in critical care research. Journal of the American Medical Informatics Association, 25(1):32–39, 2018. [30] Stephen C Johnson. Hierarchical clustering schemes. Psychometrika, 32(3):241– 254, 1967. [31] Scott Menard. Applied logistic regression analysis. Number 106. Sage, 2002. 53 [32] Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735–1780, 1997. [33] Larry Medsker and Lakhmi C Jain. Recurrent Neural Networks: Design and Applications. CRC press, 1999. [34] Tianqi Chen, Tong He, Michael Benesty, Vadim Khotilovich, Yuan Tang, Hyunsu Cho, Kailong Chen, et al. Xgboost: extreme gradient boosting. R package version 0.4-2, 1(4):1–4, 2015. [35] Gérard Biau and Erwan Scornet. A random forest guided tour. Test, 25(2):197–227, 2016. [36] Shiqi Yu, Sen Jia, and Chunyan Xu. Convolutional neural networks for hyperspectral image classification. Neurocomputing, 219:88–98, 2017. [37] Qing Li, Weidong Cai, Xiaogang Wang, Yun Zhou, David Dagan Feng, and Mei Chen. Medical image classification with convolutional neural network. In 2014 13th international conference on control automation robotics & vision (ICARCV), pages 844–848. IEEE, 2014. [38] Jiang Wang, Yi Yang, Junhua Mao, Zhiheng Huang, Chang Huang, and Wei Xu. Cnn-rnn: A unified framework for multi-label image classification. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pages 2285– 2294, 2016. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/86183 | - |
dc.description.abstract | 敗血症是加護病房內嚴重疾病之一,此疾病發病後可能導致患者的高死亡率和多種併發症。由於不同的患者、生命特徵、敗血症標準和預測方法,敗血症的早期預測是具有挑戰性。本研究旨在通過機器學習算法和深度學習方法開發一種高準確率的早期敗血症預測模型,該模型可以提高敗血症的早期預測,藉此警示醫生那些未來可能發展成敗血症的病患,從而降低發病率和死亡率。 此研究所開發的模型分類結果顯示 XGBoost 和 CNN 預測模型在分類敗血症方面表現出很強的性能。在 MIMIC-III 資料庫中,使用 SIRS 標準和XGBoost模型在敗血症發病時的病患 AUROC 約為 0.876,發病前 8 小時的 AUROC 為 0.780。使用 qSOFA標準在敗血症發病時的病患 AUROC 約為 0.942,發病前 8 小時的 AUROC 為0.729。CNN 預測模型使用 SIRS 標準在敗血症發病時達到了 0.996 AUROC,在發病前 8 小時的 AUROC 值為 0.945。 在 MIMIC-IV 資料庫中,使用 SIRS 標準和XGBoost模型在敗血症發病時的病患 AUROC 約為0.836,發病前 8 小時的 AUROC 為 0.902。使用 qSOFA 標準在敗血症發病時的病患 AUROC 約為 0.823,發病前 8 小時的AUROC 為 0.737。 CNN 預測模型使用 SIRS 標準在敗血症發病時達到了 0.992 的AUROC,在發病前 8 小時的 AUROC 值為 0.917. 和前人做法不同的地方是我將一般的數值輸入轉換成圖像輸入,並且使用圖像輸入比起數值輸入可以得到更好的分類效果。因此,相信 CNN 和 XGBoost 預測模型可以用於提前 8小時預測敗血症發作。根據本研究的結果,CNN 和 XGBoost 預測模型可以使用八個特徵提前8小時準確預測敗血症發作。此外,僅使用八個特徵就獲得了這些高準確率的早期預測結果。總之,結果顯示 CNN 和 XGBoost 預測模型在敗血症的早期預測上可以得到很好的效果。 | zh_TW |
dc.description.abstract | Sepsis is one of the severe diseases which has high mortality, multiple complications, and cost overruns among patients treated in the intensive care unit (ICU). Because of variations in different patient cohorts, clinical variables, sepsis criterion, and prediction tasks, early clinical recognition of sepsis is challenging. This study aimed to develop a high-performance early sepsis prediction model by a machine learning algorithm and deep learning method that can improve the early detection of sepsis, thereby reducing morbidity and mortality. The model classification results developed in this study show that the XGBoost and CNN prediction models exhibit strong performance in classifying sepsis. In the MIMIC-III dataset, subjects using the SIRS criterion and the XGBoost model had an AUROC of approximately 0.876 at the onset of sepsis and an AUROC of 0.780 eight hours before onset. Using the qSOFA criterion had an AUROC of 0.942 at the onset of sepsis and an AUROC of 0.729 eight hours before onset. The CNN prediction model achieved 0.996 AUROC at the onset of sepsis using the SIRS criterion and an AUROC value of 0.945 eight hours before the onset of sepsis. In the MIMIC-IV dataset, using the SIRS criterion and the XGBoost model had an AUROC of 0.836 at the onset of sepsis and an AUROC of 0.902 eight hours before onset. Subjects using the qSOFA criterion had an AUROC of approximately 0.823 at the onset of sepsis and an AUROC of 0.737 eight hours before onset. Using the SIRS criterion, the CNN prediction model achieved an AUROC of 0.992 at the onset of sepsis and an AUROC value of 0.917 eight hours before the onset of sepsis. According to the results, using eight features, the CNN and XGBoost prediction models could accurately predict sepsis onset up to eight hours in advance. Our model significantly outperformed previously existing ones. Furthermore, these high-accuracy early prediction results were obtained using only eight features. In summary, results demonstrated that the CNN and XGBoost prediction models could improve early sepsis detection. | en |
dc.description.provenance | Made available in DSpace on 2023-03-19T23:40:56Z (GMT). No. of bitstreams: 1 U0001-0508202217472600.pdf: 1891677 bytes, checksum: 6e01e59678d375f03c5ceffd1458d73a (MD5) Previous issue date: 2022 | en |
dc.description.tableofcontents | Verification Letter from the Oral Examination Committee i 摘要 iii Abstract v Contents vii List of Figures ix List of Tables xi Chapter 1 Introduction 1 Chapter 2 Literature Reviews 5 Chapter 3 Methods 9 3.1 Research Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.2 Sepsis Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.3 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.4 Patient Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.5 Data Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.6 Heatmaps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.7 Classification Models . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.7.1 Logistic regression . . . . . . . . . . . . . . . . . . . . . . . . . . 24 viidoi:10.6342/NTU202202105 3.7.2 Long short-term memory . . . . . . . . . . . . . . . . . . . . . . . 24 3.7.3 Extreme gradient boosting . . . . . . . . . . . . . . . . . . . . . . 25 3.7.4 Convolutional neural network . . . . . . . . . . . . . . . . . . . . . 26 Chapter 4 Results 27 4.1 Patient Demographics . . . . . . . . . . . . . . . . . . . . . . . . . 29 4.2 Numerical Model Results . . . . . . . . . . . . . . . . . . . . . . . 30 4.2.1 MIMIC-III . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 4.2.2 MIMIC-IV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 4.3 Heatmap Model Results . . . . . . . . . . . . . . . . . . . . . . . . 35 4.3.1 MIMIC-III . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 4.3.2 MIMIC-IV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4.4 Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 Chapter 5 Discussion 45 Chapter 6 Conclusion 47 References 49 | |
dc.language.iso | en | |
dc.title | 利用機器學習方法於 MIMIC 資料庫之早期敗血症預測 | zh_TW |
dc.title | Early Prediction of Sepsis Using Machine Learning Methods on MIMIC Database | en |
dc.type | Thesis | |
dc.date.schoolyear | 110-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 陳定立(Ting-Li Chen),王偉仲(Wei-Chung Wang) | |
dc.subject.keyword | 敗血症,早期預測,加護病房,機器學習,深度學習, | zh_TW |
dc.subject.keyword | Sepsis,Early prediction,Intensive care unit,Machine learning,Deep learning, | en |
dc.relation.page | 54 | |
dc.identifier.doi | 10.6342/NTU202202105 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2022-09-05 | |
dc.contributor.author-college | 共同教育中心 | zh_TW |
dc.contributor.author-dept | 統計碩士學位學程 | zh_TW |
dc.date.embargo-lift | 2022-09-06 | - |
顯示於系所單位: | 統計碩士學位學程 |
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