以機器學習建構新生兒健康之臨床決策支援系統

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姓名

楊涓言(Chuan-Yen Yang) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

以機器學習建構新生兒健康之臨床決策支援系統
(Implementing a Clinical Decision Support System for Newborn Health through the application of machine learning techniques)

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至系統瀏覽論文 (2030-1-1以後開放)

摘要(中)

隨著全球少子化趨勢加劇，嬰兒健康問題尤為重要，早產與低出生體重是導致新生兒死亡和發展障礙的主要因素，對家庭、社會及醫療系統造成重大負擔，且近年來，臺灣亦面臨生育率持續下降的問題，進一步加劇了新生兒健康議題的重要性，因此，如何有效預測並降低早產與低出生體重的發生，成為當前研究的一個重要方向。本研究共設計了兩階段實驗，透過分析孕產婦和新生兒的住院臨床資料，利用機器學習方法找出導致早產和低出生體重的潛在因素，實驗一針對新生兒早產的預測模型，實驗二針對新生兒低出生體重的預測模型。結果顯示，極限梯度提升樹在預測新生兒早產和低出生體重方面表現最佳，透過模型結果，最終建立新生兒健康的臨床決策系統，以作為早期識別高風險新生兒的工具，使醫療專業人員能及時進行干預和護理，提供更為精準的醫療資源配置，以減少不良健康結果的發生。

摘要(英)

With the intensification of the global trend of declining birth rates, infant health issues have become particularly important. Premature birth and low birth weight are major factors leading to newborn mortality and developmental disabilities, imposing significant burdens on families, society, and healthcare systems. In recent years, Taiwan has also faced continuously declining birth rates, further emphasizing the importance of newborn health issues. Therefore, effectively predicting and reducing the occurrence of premature birth and low birth weight has become a key focus of current research. This study designed two-stage experiments, analyzing clinical data of pregnant women and newborns to identify potential factors leading to premature birth and low birth weight using machine learning methods. The first experiment focused on a predictive model for premature birth, while the second experiment focused on a predictive model for low birth weight. The results showed that extreme gradient boosting trees performed best in predicting premature birth and low birth weight in newborns. Based on the model results, a clinical decision support system for newborn health was established. This system serves as a tool for early identification of high-risk newborns, enabling healthcare professionals to intervene and provide care promptly, allocate medical resources more accurately, and reduce the occurrence of adverse health outcomes.

關鍵字(中)

★ 機器學習
★ 低出生體重
★ 新生兒健康
★ 臨床決策系統
★ 早產

關鍵字(英)

論文目次

摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vii
表目錄 viii
第一章緒論 1
1.1 研究背景 1
1.2 研究動機 3
1.3 研究目的 5
1.4 論文架構 6
第二章文獻探討 8
2.1 早產、低出生體重之定義與發生原因探討 8
2.2 產婦和新生兒生理與臨床特徵 10
2.2.1 產婦生理與臨床特徵 10
2.2.2 新生兒臨床特徵 12
2.3 機器學習於醫療之應用 13
2.4 臨床決策系統 15
第三章研究方法 16
3.1 個案醫院介紹 16
3.2 資料收集 17
3.2.1 產婦住院資料集 17
3.2.2 新生兒住院資料集 17
3.3 研究方法與架構 19
3.4 資料前置處理 20
3.4.1 資料清理 21
3.4.2 資料整合 21
3.4.3 資料轉換 22
3.5 變數定義 24
3.5.1 自變數 24
3.5.2 依變數 26
3.6 應用於本研究之機器學習方法 27
3.6.1 決策樹（Decision Tree, DT） 27
3.6.2 羅吉斯廻歸（Logistic Regression, LR） 27
3.6.3 隨機森林（Random Forest, RF） 28
3.6.4 支持向量機（Support Vector Machine, SVM） 29
3.6.5 極限梯度提升樹（eXtreme Gradient Boosting, XGBoost） 29
3.7 實驗設計及模型驗證 30
3.7.1 實驗一設計：新生兒早產預測模型 30
3.7.2 實驗二設計：新生兒低出生體重預測模型 32
3.7.3 模型驗證 34
第四章結果與分析 37
4.1 描述性統計 37
4.2 實驗結果 43
4.2.1 實驗一 43
4.2.2 實驗二 47
4.3 建置臨床決策系統 51
第五章研究結論與建議 53
5.1 研究結論及貢獻 53
5.2 研究限制 54
5.2.1 研究數據來源的侷限性 54
5.2.2 特徵選擇與模型性能的平衡 54
5.3 未來研究方向與建議 55
參考文獻 56

參考文獻

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指導教授

曾筱珽(Hsiao-Ting Tseng)

審核日期

2024-7-26

推文