政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/153393
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113873/144892 (79%)
Visitors : 51903331      Online Users : 481
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
    •  Adv. Search
    Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer Goto mobile version


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/153393


    Title: 集成式模型應用於預測院內心臟驟停：整合多維度生命徵象數據
    Bagged Ensemble Model for In-Hospital Cardiac Arrest Prediction: Integrating Multi-Dimensional Vital Signs Data
    Authors: 翁祐祥
    Weng, Yu-Hsiang
    Contributors: 邱淑怡
    Chiu, Shu-I
    翁祐祥
    Weng, Yu-Hsiang
    Keywords: 心臟驟停
    數據不平衡
    集成式學習
    相互資訊
    cardiac arrest
    imbalanced data
    ensemble learning
    mutual information
    Date: 2024
    Issue Date: 2024-09-04 15:02:34 (UTC+8)
    Abstract: 在本研究中，我們探討了如何利用生命徵象 (vital signs) 的時序資料來預測住院病人的心臟驟停 (In-Hospital Cardiac Arrest)。我們將生命徵象的時序數據轉換為三維的特徵資料，以便更有效地捕捉時間序列中的變化模式。為了提升特徵選擇的準確性，我們採用了相互資訊 (mutual information) 作為選擇指標，以確保選出的特徵對於預測模型具有最重要的貢獻。
    面對數據不平衡 (imbalanced data) 的挑戰，我們運用了各種資料擴增方法來平衡樣本分佈，從而提高模型的泛化能力和預測準確性。接著，我們應用了多種機器學習算法來建立預測模型，並利用集成式學習 (ensemble learning) 方法進行模型的整合。這些方法的結合旨在充分發揮不同模型的優勢，提高預測的穩定性和準確性。
    實驗結果顯示，經過特徵選擇、資料擴增以及集成式學習等一系列處理後，我們所建立的模型在預測住院病人心臟驟停的準確性上均取得了顯著的提升。本研究的結果不僅展示了時序數據在預測心臟驟停方面的潛力，也為未來在此領域的研究提供了新的思路和方法。
    In this study, we investigated how to utilize time-series data of vital signs to predict in-hospital cardiac arrest. We transformed the time-series data of vital signs into three-dimensional feature data to more effectively capture the variation patterns within the time series. To enhance the accuracy of feature selection, we employed mutual information as a selection criterion to ensure that the chosen features made the most significant contribution to the predictive model.
    In addressing the challenge of imbalanced data, we used various data augmentation methods to balance the sample distribution, thereby improving the model's generalization capability and predictive accuracy. Subsequently, we applied multiple machine learning algorithms to build the prediction model and employed ensemble learning methods for model integration. The combination of these methods aimed to fully leverage the strengths of different models, enhancing prediction stability and accuracy.
    Experimental results demonstrated that after feature selection, data augmentation, and ensemble learning, the model we developed achieved significant improvements in the accuracy of predicting in-hospital cardiac arrest. The findings of this study not only highlight the potential of time-series data in predicting cardiac arrest but also provide new insights and methods for future research in this area.
    Reference: 1. Smith, G.B., et al., A Comparison of the Ability of the Physiologic Components of Medical Emergency Team Criteria and the U.K. National Early Warning Score to Discriminate Patients at Risk of a Range of Adverse Clinical Outcomes*. Critical Care Medicine, 2016. 44(12).
    2. Subbe, C.P., et al., Validation of a modified Early Warning Score in medical admissions. QJM: An International Journal of Medicine, 2001. 94(10): p. 521-526.
    3. Chang, H.K., et al. Early Detecting In-Hospital Cardiac Arrest Based on Machine Learning on Imbalanced Data. in 2019 IEEE International Conference on Healthcare Informatics (ICHI). 2019.
    4. Tanii, R., et al., Impact of dynamic parameter of trends in vital signs on the prediction of serious events in hospitalized patients -a retrospective observational study. Resuscitation Plus, 2024. 18: p. 100628.
    5. Kwon, J.m., et al., An Algorithm Based on Deep Learning for Predicting In‐Hospital Cardiac Arrest. Journal of the American Heart Association, 2018. 7(13): p. e008678.
    6. Su, C.-F., et al., Improved inpatient deterioration detection in general wards by using time-series vital signs. Scientific Reports, 2022. 12(1): p. 11901.
    7. Mahajan, P., et al. Ensemble Learning for Disease Prediction: A Review. Healthcare, 2023. 11, DOI: 10.3390/healthcare11121808.
    8. Pei, C., et al., Ensemble Learning for Early-Response Prediction of Antidepressant Treatment in Major Depressive Disorder. Journal of Magnetic Resonance Imaging, 2020. 52(1): p. 161-171.
    9. Awad, A., et al., Early hospital mortality prediction of intensive care unit patients using an ensemble learning approach. International Journal of Medical Informatics, 2017. 108: p. 185-195.
    10. Su, C.F., et al., Improved inpatient deterioration detection in general wards by using time-series vital signs. Sci Rep, 2022. 12(1): p. 11901.
    11. Vergara, J.R. and P.A. Estévez, A review of feature selection methods based on mutual information. Neural Computing and Applications, 2014. 24(1): p. 175-186.
    12. Ross, B.C., Mutual information between discrete and continuous data sets. PloS one, 2014. 9(2): p. e87357.
    13. Mohammed, R., J. Rawashdeh, and M. Abdullah. Machine Learning with Oversampling and Undersampling Techniques: Overview Study and Experimental Results. in 2020 11th International Conference on Information and Communication Systems (ICICS). 2020.
    14. Chawla, N.V., et al., SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research, 2002. 16: p. 321-357.
    15. He, H., et al. ADASYN: Adaptive synthetic sampling approach for imbalanced learning. in 2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence). 2008. Ieee.
    16. Kumari, S., D. Kumar, and M. Mittal, An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier. International Journal of Cognitive Computing in Engineering, 2021. 2: p. 40-46.
    17. Salur, M.U. and İ. Aydın, A soft voting ensemble learning-based approach for multimodal sentiment analysis. Neural Computing and Applications, 2022. 34(21): p. 18391-18406.
    Description: 碩士
    國立政治大學
    資訊科學系
    111753226
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0111753226
    Data Type: thesis
    Appears in Collections:[Department of Computer Science ] Theses

    Files in This Item:

    File SizeFormat
    322601.pdf1077KbAdobe PDF0View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback