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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 歐陽彥正 | zh_TW |
| dc.contributor.advisor | Yen-Jen Oyang | en |
| dc.contributor.author | 劉信宏 | zh_TW |
| dc.contributor.author | Hsin-Hung Liu | en |
| dc.date.accessioned | 2023-09-07T16:14:34Z | - |
| dc.date.available | 2025-08-01 | - |
| dc.date.copyright | 2023-09-11 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-08-08 | - |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/89256 | - |
| dc.description.abstract | 評估急性腎損傷風險一直是重症加護病房內的臨床醫師所面臨的一個具有挑戰性的問題。近年來許多研究已進行於調查多種血清電解質與急性腎損傷之間的關聯。然而，血清肌酐、血尿素氮和臨床相關血清電解質的複合效應尚未得到全面調查。
因此我們啟動了這項研究，旨在開發能夠闡明這些因素彼此之間相互作用的機器學習模型。特別地，我們專注於沒有先前急性腎損傷記錄或急性腎損傷相關共病的重症加護病房患者。透過這種方法，我們能夠以更受控的方式檢視血清電解質水平與腎功能之間的關聯。 通過我們仔細的分析，我們發現血清肌酐、氯離子和鎂離子濃度是這個特定患者群體需要密切監測的三個主要因素。 總之，我們的研究結果不僅為制定早期干預和有效管理策略提供了有價值的見解，還為未來研究探討所涉及的病理生理機制提供了關鍵線索。對於未來的研究，應進行基於不同急性腎損傷原因的亞組分析，以進一步增進我們對急性腎損傷的理解。 | zh_TW |
| dc.description.abstract | Assessing the risk of acute kidney injury (AKI) has been a challenging issue for clinicians in intensive care units (ICU). In recent years, a number of studies have been conducted to investigate the associations between several serum electrolytes and AKI.
Nevertheless, the compound effects of serum creatinine, blood urea nitrogen (BUN), and clinically relevant serum electrolytes have yet to be comprehensively investigated. Accordingly, we initiated this study aiming to develop machine learning models that illustrate how these factors interact with each other. In particular, we have focused on the ICU patients without prior history of AKI or AKI-related comorbidities. With this practice, we were able to examine the associations between the levels of serum electrolytes and renal function in a more controlled manner. Our analyses revealed that the levels of serum creatinine, chloride, and magnesium were the three major factors to be monitored for this group of patients. In summary, our results can provide not only valuable insights for developing early intervention and effective management strategies but also crucial clues for future investigation of the pathophysiological mechanisms involved. For future studies, subgroup analyses based on different causes of AKI should be conducted to further enhance our understanding of AKI. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-09-07T16:14:34Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-09-07T16:14:34Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii Acknowledgements iii 中文摘要 v Abstract vi Table of Contents viii List of Figures X List of Tables X List of Supplementary Materials X Chapter 1 Introduction 1 1.1 Background 1 1.2 Aim of the Study 2 1.3 Structure of the Doctoral Dissertation 2 Chapter 2 Literature Review 4 2.1 Electrolytes and AKI 4 2.2 Machine Learning and AKI 6 2.3 Lack of Comprehensive Investigation 6 2.4 Comorbidity 7 2.5 Machine Learning Models 7 Chapter 3 Materials and Methods 10 3.1 Study Cohort 10 3.1.1 MIMIC-IV dataset 10 3.1.2 Flow of Study Cohort 10 3.1.3 Pre-renal, Renal, and Post-renal 12 3.2 Machine Learning Models 18 Chapter 4 Results 20 4.1 Sensitivity 0.95 and 0.80 20 4.2 Performance 20 4.3 DT Models and Three Major Factors 21 Chapter 5 Discussion 26 Chapter 6 Conclusion and Future Work 33 References 34 Supplementary Materials 42 | - |
| dc.language.iso | en | - |
| dc.subject | 血清電解質 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 急性腎損傷 | zh_TW |
| dc.subject | 重症加護病房 | zh_TW |
| dc.subject | serum electrolyte | en |
| dc.subject | machine learning | en |
| dc.subject | intensive care unit | en |
| dc.subject | acute kidney injury | en |
| dc.title | 利用機器學習技術研究重症加護病房中血清肌酐和電 解質對急性腎損傷風險的複合影響 | zh_TW |
| dc.title | Exploiting Machine Learning Technologies to Study the Compound Effects of Serum Creatinine and Electrolytes on the Risk of Acute Kidney Injury in Intensive Care Units | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.oralexamcommittee | 賴飛羆;楊孟翰;黃乾綱;陳倩瑜;孫維仁;張瑞峰 | zh_TW |
| dc.contributor.oralexamcommittee | Feipei Lai;Meng-Han Yang;Chien-Kang Huang;Chien-Yu Chen;Wei-Zen Sun;Ruey-Feng Chang | en |
| dc.subject.keyword | 急性腎損傷,血清電解質,重症加護病房,機器學習, | zh_TW |
| dc.subject.keyword | acute kidney injury,serum electrolyte,intensive care unit,machine learning, | en |
| dc.relation.page | 45 | - |
| dc.identifier.doi | 10.6342/NTU202302952 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2023-08-09 | - |
| 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 | - |
| dc.date.embargo-lift | 2025-08-04 | - |
| 顯示於系所單位： | 生醫電子與資訊學研究所 | |
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