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題名:加護病房患者死亡率與再入院預測 – 人工智慧與主題模型之應用
作者:簡德年
作者(外文):CHIEN, TE-NIEN
校院名稱:國立臺北科技大學
系所名稱:管理學院管理博士班
指導教授:吳忠敏
邱志洲
學位類別:博士
出版日期:2023
主題關鍵詞:主題模型加護病房電子健康記錄深度學習機器學習Topic ModelIntensive Care UnitsElectronic Health RecordsMachine LearningDeep Learning
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在加護病房相關研究中,臨床病患生命徵象預測一直是臨床與學術研究的重要議題。近年來,關於患者死亡率預測和患者再入院預測的研究逐漸增多。然而,在過去的研究中,對於電子健康記錄資料的分析主要集中在數值型資料,而對於文字型資料的研究相對較少。本研究利用MIMIC-III數據庫中收集到的結構化資料和半結構化資料(包括病患的臨床記錄、診斷資料和檢驗報告),結合自然語言處理技術中的LDA模型和BERTopic模型,以及機器學習和深度學習的方法,對加護病房中患者的死亡率和出院後再入院的預測進行研究。我們的研究建構的模型整合了結構化資料和半結構化資料,不僅提高了預測的準確性,也提升了預測效能,同時成功辨識出重要的預測因子。我們期望這樣的分析結果能夠增加醫護人員對於病患病情的掌握,實現早期干預和護理管理來改善患者的治療效果並降低醫療保健成本,同時讓醫療資源能夠得到更優化的應用。
In intensive care unit (ICU)-related research, the prediction of vital signs in clinical patients has always been a pivotal topic in both clinical and academic studies. Recent years have witnessed a growing number of studies focusing on predicting patient mortality and readmission rates. However, the majority of previous research has primarily concentrated on analyzing numerical data from electronic health records, with limited attention given to semi-structured data analysis. This study aims to address this gap by leveraging the MIMIC-III database, which comprises structured and semi-structured data encompassing clinical records, diagnostic information, and test reports. By incorporating natural language processing techniques such as the Latent Dirichlet Allocation (LDA) model and the BERTopic model, as well as machine learning and deep learning methods, we endeavor to predict mortality rates and readmission following discharge for adult patients in the ICU. Our research constructed a model that integrates structured and semi-structured data, leading to improved prediction accuracy and performance, as well as successful identification of significant predictive factors. We anticipate that such analytical results can enhance healthcare professionals' understanding of patients' conditions, enabling early intervention and care management to improve treatment outcomes and reduce healthcare costs, while optimizing the utilization of medical resources.
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