緊急醫療救護體系，由消防端之到院前緊急救護服務，及醫療端之到院後緊急醫療服務共同組成。由行政院內政部消防署及各直轄市、縣（市）政府消防局共同合作，設置119救護車及救護技術員（Emergency Medical Technician; EMT），提供24小時到院前緊急救護服務。臺灣雖地狹人稠，然為積極搶救生命，仍需要迅捷的緊急救護服務。
需要啟動到院前緊急救護體系之病症非常多樣，其中急迫程度最高、亟需與時間賽跑的病症之一即為到院前心肺功能停止（Out-of-hospital cardiac arrest; OHCA）。目前我國到院前救護針對OHCA患者之送醫，多限制不得指定醫院，並以就近送醫為主，並無資料科學基礎。
本研究以高雄市非創傷之成人OHCA患者資料訓練機器學習模型，藉由評估羅吉斯迴歸、正規化迴歸、決策樹、隨機森林、支持向量機與極限梯度提升之機器學習模型，提供患者存活率之預測建議。若患者可於到院前恢復循環，則可考慮送至可執行低溫治療之醫療院所，希可提高患者出院存活率及改善神經學預後，藉此探討以機器學習模型為基礎，發展醫療決策支援系統（Clinical Decision Support System；CDSS）之可行性，期能做為未來EMT送醫之參考基礎。
本研究建立之機器學習模型，AUC及精確率均以隨機森林最佳，敏感度及特異性則分別為決策樹與SVM表現較好；共8種機器學習演算法中，精確率均有相當改善空間，然部分演算法具有相當高的特異性，如能進一步透過特徵工程、增加資料集等提升精確率，則可能有未來應用價值。

The emergency medical service system in Taiwan consists of both pre-hospital emergency medical services provided by the fire department, and hospital-based emergency care. The National Fire Agency of the Ministry of the Interior and the fire departments of each city cooperate in setting up 119 system with ambulances and Emergency Medical Technicians (EMTs) to provide 24-hour pre-hospital emergency medical services. Although Taiwan is small and densely populated, it still requires rapid emergency medical services in order to actively save lives.
There are a wide variety of medical conditions that require activation of the pre-hospital emergency medical services, with one of the most urgent and time-sensitive being Out-of-hospital cardiac arrest (OHCA). Currently, in Taiwan, pre-hospital emergency medical services for OHCA patients often impose restrictions on hospital designation and prioritize nearby hospital transport, without a foundation in data science.
This study trained machine learning models on data from non-traumatic adult Out-of-hospital cardiac arrest (OHCA) patients in Kaohsiung City. By evaluating various machine learning models including logistic regression, regularized regression, decision tree, random forest, support vector machine, and extreme gradient boosting, the study aims to provide predictive recommendations for patient survival rates. If a patient achieves return of spontaneous circulation before arrival at the hospital, consideration can be given to transferring them to a hospital where therapeutic hypothermia can be performed, in order to improve the patient's discharge survival rate and neurological prognosis. The study explores the feasibility of developing a Clinical Decision Support System(CDSS) based on machine learning models, in order to provide a scientific reference basis for future EMT transportation decisions.
The machine learning model established in this study showed that random forest had the best performance in terms of AUC and precision, while decision tree and SVM performed better in terms of sensitivity and specificity, respectively. Among the eight machine learning algorithms tested in this study showed considerable improvement in precision rates. However, some algorithms exhibited high specificity. If precision rates can be further improved through feature engineering or increasing the dataset, these algorithms may have future applications.

論文審定書 i
誌謝 ii
摘要 iii
ABSTRACT iv
第1章 緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 3
第2章 文獻探討 5
2.1 到院前心肺功能停止存活率之影響因子 5
2.2 低溫治療 8
第3章 研究方法 9
3.1 研究架構 9
3.2 特徵描述 10
3.3 機器學習模型 15
3.3.1 羅吉斯迴歸 15
3.3.2 正規化迴歸 15
3.3.3 決策樹 15
3.3.4 隨機森林 16
3.3.5 支持向量機 16
3.3.6 極限梯度提升 16
3.4 模型效能評估 16
第4章 研究結果與分析 19
4.1 資料蒐集 19
4.2 資料前處理 20
4.3 建立模型 22
4.4 模型效能比較 22
第5章 結論與建議 25
5.1 結論 25
5.2 研究限制 25
5.3 建議 26
參考文獻 26

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