請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88337
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 賴飛羆 | zh_TW |
dc.contributor.advisor | Fei-pei Lai | en |
dc.contributor.author | 張裕鑫 | zh_TW |
dc.contributor.author | Yu-Hsin Chang | en |
dc.date.accessioned | 2023-08-09T16:36:35Z | - |
dc.date.available | 2023-11-09 | - |
dc.date.copyright | 2023-08-09 | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2023-07-19 | - |
dc.identifier.citation | 1. Lone, N.I. and T.S. Walsh, Prolonged mechanical ventilation in critically ill patients: epidemiology, outcomes and modelling the potential cost consequences of establishing a regional weaning unit. Critical Care, 2011. 15: p. 1-10.
2. Zilberberg, M.D., et al., Characteristics, Hospital Course, and Outcomes of Patients Requiring Prolonged Acute Versus Short-Term Mechanical Ventilation in the United States, 2014–2018*. Critical Care Medicine, 2020. 48(11): p. 1587-1594. 3. Cabello, B., et al., Physiological comparison of three spontaneous breathing trials in difficult-to-wean patients. Intensive Care Med, 2010. 36(7): p. 1171-9. 4. dos Santos Bien, U., et al., Maximum inspiratory pressure and rapid shallow breathing index as predictors of successful ventilator weaning. Journal of physical therapy science, 2015. 27(12): p. 3723-3727. 5. Duan, J., X. Zhang, and J. Song, Predictive power of extubation failure diagnosed by cough strength: a systematic review and meta-analysis. Crit Care, 2021. 25(1): p. 357. 6. Jaber, S., et al., Risk factors and outcomes for airway failure versus non-airway failure in the intensive care unit: a multicenter observational study of 1514 extubation procedures. Crit Care, 2018. 22(1): p. 236. 7. Thille, A.W., et al., Risk Factors for and Prediction by Caregivers of Extubation Failure in ICU Patients: A Prospective Study*. Critical Care Medicine, 2015. 43(3): p. 613-620. 8. Chung, W.C., et al., Novel mechanical ventilator weaning predictive model. Kaohsiung J Med Sci, 2020. 36(10): p. 841-849. 9. Xie, J., et al., To extubate or not to extubate: Risk factors for extubation failure and deterioration with further mechanical ventilation. J Card Surg, 2019. 34(10): p. 1004-1011. 10. Blackwood, B., et al., Use of weaning protocols for reducing duration of mechanical ventilation in critically ill adult patients: Cochrane systematic review and meta-analysis. BMJ, 2011. 342: p. c7237. 11. Thille, A.W., et al., Outcomes of extubation failure in medical intensive care unit patients. Crit Care Med, 2011. 39(12): p. 2612-8. 12. Shaffer, F. and J.P. Ginsberg, An Overview of Heart Rate Variability Metrics and Norms. Front Public Health, 2017. 5: p. 258. 13. Tiainen, M., et al., Arrhythmias and heart rate variability during and after therapeutic hypothermia for cardiac arrest. Crit Care Med, 2009. 37(2): p. 403-9. 14. Ranard, B.L., et al., Heart rate variability and adrenal size provide clues to sudden cardiac death in hospitalized COVID-19 patients. J Crit Care, 2022. 71: p. 154114. 15. Gupta, A.K. Respiration Rate Measurement Based on Impedance Pneumography. 2011. 16. Seppä, V.-P., et al., Impedance pneumography for assessment of a tidal breathing parameter in patients with airway obstruction. European Respiratory Journal, 2011. 38(Suppl 55): p. p1199. 17. Seppa, V.P., J. Viik, and J. Hyttinen, Assessment of pulmonary flow using impedance pneumography. IEEE Trans Biomed Eng, 2010. 57(9): p. 2277-85. 18. Arcentales, A., et al., Classification of patients undergoing weaning from mechanical ventilation using the coherence between heart rate variability and respiratory flow signal. Physiological Measurement, 2015. 36(7): p. 1439-1452. 19. Huang, C.-T., et al., Application of heart-rate variability in patients undergoing weaning from mechanical ventilation. Critical Care, 2014. 18(1): p. R21. 20. Chaparro, J.A. and B.F. Giraldo. Power index of the inspiratory flow signal as a predictor of weaning in intensive care units. in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2014. IEEE. 21. Seely, A.J., et al., Do heart and respiratory rate variability improve prediction of extubation outcomes in critically ill patients? Critical Care, 2014. 18(2): p. 1-12. 22. Arcentales, A., et al., Classification of patients undergoing weaning from mechanical ventilation using the coherence between heart rate variability and respiratory flow signal. Physiol Meas, 2015. 36(7): p. 1439-52. 23. Seely, A.J., et al., Do heart and respiratory rate variability improve prediction of extubation outcomes in critically ill patients? Crit Care, 2014. 18(2): p. 1-12. 24. Bien, M.Y., et al., Comparisons of predictive performance of breathing pattern variability measured during T-piece, automatic tube compensation, and pressure support ventilation for weaning intensive care unit patients from mechanical ventilation. Crit Care Med, 2011. 39(10): p. 2253-62. 25. Engoren, M. and J.M. Blum, A comparison of the rapid shallow breathing index and complexity measures during spontaneous breathing trials after cardiac surgery. J Crit Care, 2013. 28(1): p. 69-76. 26. Hsieh, M.H., et al., An Artificial Neural Network Model for Predicting Successful Extubation in Intensive Care Units. J Clin Med, 2018. 7(9). 27. Kuo, H.J., et al., Improvement in the Prediction of Ventilator Weaning Outcomes by an Artificial Neural Network in a Medical ICU. Respir Care, 2015. 60(11): p. 1560-9. 28. Chen, T., et al., Prediction of Extubation Failure for Intensive Care Unit Patients Using Light Gradient Boosting Machine. IEEE Access, 2019. 7: p. 150960-150968. 29. Zhao, Q.Y., et al., Development and Validation of a Machine-Learning Model for Prediction of Extubation Failure in Intensive Care Units. Front Med (Lausanne), 2021. 8: p. 676343. 30. Johnson, A.E., et al., MIMIC-III, a freely accessible critical care database. Sci Data, 2016. 3: p. 160035. 31. Goldberger, A.L., et al., PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. circulation, 2000. 101(23): p. e215-e220. 32. Boles, J.M., et al., Weaning from mechanical ventilation. Eur Respir J, 2007. 29(5): p. 1033-56. 33. Schafer, A. and K.W. Kratky, Estimation of breathing rate from respiratory sinus arrhythmia: comparison of various methods. Ann Biomed Eng, 2008. 36(3): p. 476-85. 34. Charlton, P.H., et al., An impedance pneumography signal quality index: Design, assessment and application to respiratory rate monitoring. Biomed Signal Process Control, 2021. 65: p. 102339. 35. Sedghamiz, H., Complete Pan Tompkins Implementation ECG QRS detector. March 2014, MATLAB Central File Exchange. Avalible online: https://www.mathworks.com/matlabcentral/fileexchange/45840-complete-pan-tompkins-implementation-ecg-qrs-detectorRetrieved ( Retrieved February 20, 2023). 36. Pan, J. and W.J. Tompkins, A real-time QRS detection algorithm. IEEE transactions on biomedical engineering, 1985(3): p. 230-236. 37. Zhao, L., et al., Influence of Ectopic Beats on Heart Rate Variability Analysis. Entropy (Basel), 2021. 23(6). 38. Catai, A.M., et al., Heart rate variability: are you using it properly? Standardisation checklist of procedures. Braz J Phys Ther, 2020. 24(2): p. 91-102. 39. Electrophysiology, T.F.o.t.E.S.o.C.t.N.A.S.o.P., Heart Rate Variability. Circulation, 1996. 93(5): p. 1043-1065. 40. Clifford, G.D., P.E. McSharry, and L. Tarassenko. Characterizing artefact in the normal human 24-hour RR time series to aid identification and artificial replication of circadian variations in human beat to beat heart rate using a simple threshold. in Computers in Cardiology. 2002. 41. McKinley, S. and M. Levine, Cubic spline interpolation. College of the Redwoods, 1998. 45(1): p. 1049-1060. 42. Guyon, I. and A. Elisseeff, An introduction to variable and feature selection. Journal of machine learning research, 2003. 3(Mar): p. 1157-1182. 43. Chawla, N.V., et al., SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research, 2002. 16: p. 321-357. 44. Chen, T. and C. Guestrin. Xgboost: A scalable tree boosting system. in Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 2016. 45. Prokhorenkova, L., et al., CatBoost: unbiased boosting with categorical features. Advances in neural information processing systems, 2018. 31. 46. Ke, G., et al., Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 2017. 30. 47. Breiman, L., Random forests. Machine learning, 2001. 45: p. 5-32. 48. Hosmer Jr, D.W., S. Lemeshow, and R.X. Sturdivant, Applied logistic regression. 3rd ed. Vol. 398. 2013, Hoboken, New Jersey: John Wiley & Sons. 49. DeLong, E.R., D.M. DeLong, and D.L. Clarke-Pearson, Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics, 1988: p. 837-845. 50. Sun, X. and W. Xu, Fast Implementation of DeLong’s Algorithm for Comparing the Areas Under Correlated Receiver Operating Characteristic Curves. IEEE Signal Processing Letters, 2014. 21(11): p. 1389-1393. 51. Lundberg, S.M. and S.-I. Lee, A unified approach to interpreting model predictions. Advances in neural information processing systems, 2017. 30. 52. Bisong, E. and E. Bisong, Google colaboratory. Building machine learning and deep learning models on google cloud platform: a comprehensive guide for beginners, 2019: p. 59-64. 53. Mlynczak, M., et al., Assessment of calibration methods on impedance pneumography accuracy. Biomed Tech (Berl), 2016. 61(6): p. 587-593. 54. Otaguro, T., et al., Machine Learning for Prediction of Successful Extubation of Mechanical Ventilated Patients in an Intensive Care Unit: A Retrospective Observational Study. J Nippon Med Sch, 2021. 88(5): p. 408-417. 55. Wang, H.-b., et al., A Robust Electrode Configuration for Bioimpedance Measurement of Respiration. Journal of Healthcare Engineering, 2014. 5: p. 696103. | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88337 | - |
dc.description.abstract | 背景: 侵入性機械通氣仍然是作為拯救重症患者生命的主要治療方法。然而,過久的侵入性機械通氣可能會增加呼吸器相關肺炎的風險,與增加醫院費用和死亡率。為評估是否適合脫離機械通氣,必須進行多因素評估,包括生理狀態、鎮靜劑使用、通氣機設置、插管的主要原因是否緩解,以及是否擁有通過自發性呼吸試驗的能力。不論是延長侵入性機械通氣的時間或因為拔管失敗都會產生相對應的風險,也因此,確定拔管的時機非常重要。到目前為止,還沒有關於拔管的統一評估準則。
目標: 本研究的主要目的是調查:(1)在自主呼吸訓練期間的電阻式呼吸訊號與心律變異度所提取的特徵,是否能夠用於訓練機器學習模型來預測拔管失敗;以及(2)綜合結構化數據和上述信號特徵的模型是否能夠優於僅使用結構化數據的模型。 方法: 本研究乃利用已公開的美國大型資料庫,分別被稱為多參數智能監測重症監護 (Multi-parameter Intelligent Monitoring in Intensive Care (MIMIC-III)) 和MIMIC-III匹配的波形數據庫。其中包含各種資訊,包括結構化和波形數據(電阻式呼吸訊號和心電圖)。納入已進行自主呼吸試驗並具有可用波形信號的患者。利用波形分析來提取電阻式呼吸訊號的特徵,和心律變異度分析來萃取心電圖的資訊。使用五種常見的機器學習模型進行拔管失敗的預測。此外,我們使用不同的數據類型組合來訓練模型,以評估它們對性能的影響。使用DeLong test的檢驗比較不同模型之間的AUROC,並且用Shapley value來解釋特徵對於模型預測的影響程度。 結果: 最終我們納入了 411 位滿足所有標準的拔管患者,有 46 位患者(11.2%)拔管失敗。我們發現,當使用所有的數據,包括電阻式呼吸訊號、心律變異度和結構化數據進行訓練時,XGBoost分類器優於其他分類器,並且比使用單獨的數據類型(僅結構化數據、僅電阻式呼吸訊號、僅心律變異度或綜合電阻式呼吸訊號和心律變異度)進行訓練的 XGBoost 分類器具有顯著更高的成效。特徵重要性分析顯示,電阻式呼吸訊號和心律變異度的特徵佔了 XGBoost 分類器前二十個重要特徵的一半以上。 結論:這些發現表明,將從電阻式呼吸訊號和心律變異度獲取的特徵納入機器學習模型,可以提高預測機械通氣下重症患者拔管失敗的成效。然而,需要進一步研究以驗證這些結果在不同臨床背景下的有效性。 | zh_TW |
dc.description.abstract | Background: A multifaceted evaluation is necessary for weaning patients off mechanical ventilation (MV), and identifying the optimal time for extubation is crucial. The objective of this study was to explore whether impedance pneumography (IP) and heart rate variability (HRV) features could be utilized to train or improve a machine learning (ML) model for predicting post-extubation failure (PF) in mechanically ventilated patients in the intensive care unit.
Methods: The MIMIC-III and MIMIC-III Waveform Database Matched Subset were the sources of data for this study, which consisted of clinical and waveform data, including IP signals and electrocardiogram (ECG) readings. Patients who underwent spontaneous breathing trials and had available waveform signals before extubation were eligible for inclusion. After a sequential assessment of waveform quality, the IP signal features were extracted, and HRV analysis was used to present the ECG features. Five common machine learning models were trained to predict PF. Moreover, the impact of different combinations of data types on the model's performance was evaluated. DeLong's test was utilized to compare the AUROC among individual models, and the SHapley Additive exPlanations method was applied to evaluate the models' expandability. Results: The final cohort included 411 extubated patients who met all the criteria, of whom 46 patients (11.2%) failed extubation. Our findings indicated that XGBoost classifier performed better than other classifiers when trained with the entire dataset (including IP signal, HRV, and clinical data) and produced a significantly higher AUROC than when it was trained using only one type of data (clinical data alone, IP signal alone, HRV alone, or both IP signal and HRV). Feature importance analysis revealed that IP waveform and HRV features constituted over half of the top 20 important features of the XGBoost classifier. Conclusion: The results indicate that integrating IP signal and HRV features can enhance the performance of ML models in predicting PF among critically ill patients on MV. Nevertheless, additional research is required to verify these outcomes in diverse clinical scenarios. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-09T16:36:34Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2023-08-09T16:36:35Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | CONTENTS
誌謝 i 中文摘要 ii ABSTRACT iv CONTENTS vi LIST OF FIGURES viii LIST OF TABLES ix Chapter 1 Introduction 1 1.1 Background 1 1.2 Motivation 4 1.3 Objectives 4 Chapter 2 Method 5 2.1 Data Source 5 2.2 Selection of Participation and Definition 5 2.3 Preprocessing 6 2.3.1 Clinical data 6 2.3.2 IP signal 10 2.3.3 HRV signal 20 2.4 Feature Selection 22 2.5 Training Pipeline 23 2.6 Performance Evaluation 25 2.7 Experiment Environment 26 Chapter 3 Result 27 3.1 Participant Characteristics 27 3.2 Performance 32 3.3 Feature Importance 35 3.4 XGBoost Classifier with Different Input 36 Chapter 4 Discussion 40 4.1 Clinical Application 42 4.2 Limitation 43 Chapter 5 Conclusion 45 Reference 46 Appendix 52 List of abbreviations 52 Ethics approval and consent to participate 54 Availability of data and materials 54 Competing interests 54 Funding 54 | - |
dc.language.iso | en | - |
dc.title | 機器學習應用於MIMIC III資料庫,使用呼吸訓練時呼吸電阻、心律變異與臨床資訊預測拔管成敗 | zh_TW |
dc.title | Prediction of Post-Extubation Failure using Machine Learning with Impedance Pneumography, Heart Rate Variability, and Clinical Data during Spontaneous Breathing Trial: Analysis of the MIMIC-III Database | en |
dc.type | Thesis | - |
dc.date.schoolyear | 111-2 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 蔣榮先;蔡兆勳;呂宗謙;黃冠棠 | zh_TW |
dc.contributor.oralexamcommittee | Jung-Hsien Chiang;Jaw-Shiun Tsai;TSUNG-CHIEN LU;Guan-Tarn Huang | en |
dc.subject.keyword | 人工智慧,機器學習,預測拔管失敗,加護病房,電阻式呼吸波形,心律變異度,MIMIC-III 資料庫, | zh_TW |
dc.subject.keyword | Artificial intelligence,Machine learning,Extubation failure prediction,Intensive care unit,Impedance pneumography,Heart rate variability,MIMIC-III database, | en |
dc.relation.page | 54 | - |
dc.identifier.doi | 10.6342/NTU202301628 | - |
dc.rights.note | 同意授權(全球公開) | - |
dc.date.accepted | 2023-07-19 | - |
dc.contributor.author-college | 電機資訊學院 | - |
dc.contributor.author-dept | 生醫電子與資訊學研究所 | - |
顯示於系所單位: | 生醫電子與資訊學研究所 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-111-2.pdf | 1.71 MB | Adobe PDF | 檢視/開啟 |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。