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研究生： 王河雨
Wang, Ho-Yu
論文名稱： 為住院病患惡化及院內感染制定資料驅動之早期預警方案
A Data-Driven Early Warning Scheme for Inpatient Deterioration and Healthcare-Associated Infection
指導教授： 鄧維光
Teng, Wei-Guang
學位類別： 碩士
Master
系所名稱： 工學院 - 工程科學系
Department of Engineering Science
論文出版年： 2024
畢業學年度： 112
語文別： 英文
論文頁數： 57
中文關鍵詞： 住院病患惡化 、院內感染 、早期預警系統 、長短期記憶模型 、大語言模型 、檢索增強生成
外文關鍵詞： inpatient deterioration, healthcare-associated infections, early warning system, long short-term memory, large language model, retrieval-augmented generation
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    • Chapter 1 Introduction 1 1.1 Motivation and Overview 1 1.2 Contributions of This Work 2 Chapter 2 Preliminaries 3 2.1 Inpatient Deterioration 3 2.2 Typical Early Warning Scores 5 2.2.1 Early Warning Scores Used in General Wards 5 2.2.2 Early Warning Scores for Specific Purpose 7 2.2.3 Advantages and Challenges of Early Warning Scores 9 2.3 Deep Learning-based EWS 10 2.3.1 Time-Series Data in Clinical Settings 11 2.3.2 Building Time-Series Data Model 12 2.3.3 Review of Literature on Deep Learning-based EWS 12 2.4 Healthcare-Associated Infection Surveillance 14 2.5 Utilizing NLP to Process Unstructured Data 14 2.6 Building an Explainable Model 17 Chapter 3 Our Proposed Early Warning Scheme 18 3.1 Design of Our Proposed Scheme 18 3.2 Predicting Inpatient Deterioration Using Structured Data in Clinical Settings 21 3.2.1 Building a Deterioration Prediction Model 22 3.2.2 Explanation of Predicting Inpatient Deterioration 23 3.3 Utilizing LLMs for HAI Surveillance 24 Chapter 4 Prototyping and Experimental Studies 27 4.1 Predicting Inpatient Deterioration 27 4.1.1 Dataset Used in Our Experiments 27 4.1.2 Predictive Models Experiments 28 4.1.3 SHAP for Predictive Model Interpretability 30 4.2 LLM Prototype for HAI Surveillance 36 4.3 Discussion of Our Experimental Studies 42 Chapter 5 Conclusions and Future Work 44 Bibliography 45

    [1] RCoP, L. "National Early Warning Score (NEWS): standardising the assessment of acute-illness severity in the NHS." Report of working party. London: Royal College of Physicians. 2012.
    [2] Petersen, John Asger, Lars S. Rasmussen, and Susan Rydahl-Hansen. "Barriers and facilitating factors related to use of early warning score among acute care nurses: a qualitative study." BMC emergency medicine 17 (2017): 1-9.
    [3] Kwon, Joon‐myoung, et al. "An algorithm based on deep learning for predicting in‐hospital cardiac arrest." Journal of the American Heart Association 7.13 (2018): e008678.
    [4] Goh, Kim Huat, et al. "Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare." Nature communications 12.1 (2021): 711.
    [5] Tan, Armand Delo Antone, Chito Caimoy Permejo, and Ma Consolacion Dolor Torres. "Modified Early Warning Score vs Cardiac Arrest Risk Triage Score for Prediction of Cardiopulmonary Arrest: A Case–Control Study." Indian Journal of Critical Care Medicine: Peer-reviewed, Official Publication of Indian Society of Critical Care Medicine 26.7 (2022): 780.
    [6] Chand, Manish, et al. "How and why do we measure surgical risk?." Journal of the Royal Society of Medicine 100.11 (2007): 508-512.
    [7] Jones, Alan E., Stephen Trzeciak, and Jeffrey A. Kline. "The Sequential Organ Failure Assessment score for predicting outcome in patients with severe sepsis and evidence of hypoperfusion at the time of emergency department presentation." Critical care medicine 37.5 (2009): 1649-1654.
    [8] Le Gall, Jean-Roger, Stanley Lemeshow, and Fabienne Saulnier. "A new simplified acute physiology score (SAPS II) based on a European/North American multicenter study." Jama 270.24 (1993): 2957-2963.
    [9] Gardner-Thorpe, J., et al. "The value of Modified Early Warning Score (MEWS) in surgical in-patients: a prospective observational study." The Annals of The Royal College of Surgeons of England 88.6 (2006): 571-575.
    [10] Bell DJ. National Early Warning Score (NEWS) Radiopaedia. 2019. Nov 19, [2023-01-06]. https://radiopaedia.org/articles/national-early-warning-score-news?lang=us .
    [11] Su, Chang-Fu, et al. "Improved inpatient deterioration detection in general wards by using time-series vital signs." Scientific Reports 12.1 (2022): 11901.
    [12] Shamout, Farah E., et al. "Deep interpretable early warning system for the detection of clinical deterioration." IEEE journal of biomedical and health informatics 24.2 (2019): 437-446.
    [13] Morid, Mohammad Amin, Olivia R. Liu Sheng, and Joseph Dunbar. "Time series prediction using deep learning methods in healthcare." ACM Transactions on Management Information Systems 14.1 (2023): 1-29.
    [14] Ko, Ryoung-Eun, et al. "Deep Learning-Based Early Warning Score for Predicting Clinical Deterioration in General Ward Cancer Patients." Cancers 15.21 (2023): 5145.
    [15] Kim, Jeongmin, et al. "Predicting cardiac arrest and respiratory failure using feasible artificial intelligence with simple trajectories of patient data." Journal of clinical medicine 8.9 (2019): 1336.
    [16] Raghupathi, Wullianallur, and Viju Raghupathi. "Big data analytics in healthcare: promise and potential." Health information science and systems 2 (2014): 1-10.
    [17] Wiemken, Timothy L., and Ruth M. Carrico. "Assisting the infection preventionist: Use of artificial intelligence for health care–associated infection surveillance." American Journal of Infection Control 52.6 (2024): 625-629.
    [18] Polak, Maciej P., et al. "Flexible, model-agnostic method for materials data extraction from text using general purpose language models." Digital Discovery 3.6 (2024): 1221-1235.
    [19] Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. "" Why should i trust you?" Explaining the predictions of any classifier." Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 2016.
    [20] Lauritsen, Simon Meyer, et al. "Explainable artificial intelligence model to predict acute critical illness from electronic health records." Nature communications 11.1 (2020): 3852.
    [21] Hochreiter, S. "Long Short-term Memory." Neural Computation MIT-Press (1997).
    [22] Chung, Junyoung, et al. "Empirical evaluation of gated recurrent neural networks on sequence modeling." arXiv preprint arXiv:1412.3555 (2014).
    [23] Lundberg, Scott. "A unified approach to interpreting model predictions." arXiv preprint arXiv:1705.07874 (2017).
    [24] Sina Nazeri. (2023, March 23). Unboxing the Black Box: A Guide to Explainability Techniques for Machine Learning Models Using SHAP. https://medium.com/the-power-of-ai/machine-learning-explainability-with-shap-ee60fbfa21e
    [25] Gao, Yunfan, et al. "Retrieval-augmented generation for large language models: A survey." arXiv preprint arXiv:2312.10997 (2023).
    [26] Hsu, Chan-Jan, et al. "Breeze-7B Technical Report." arXiv preprint arXiv:2403.02712 (2024).

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