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研究生:李成聿
研究生(外文):Lee, Cheng-Yu
論文名稱:用於加護病房再住院預測的多模組架構
論文名稱(外文):A Multimodal Architecture for ICU Readmission Prediction
指導教授:陳良弼陳良弼引用關係沈之涯
指導教授(外文):Chen, Arbee L.P.Shen, Chih-Ya
口試委員:吳宜鴻徐嘉連
口試委員(外文):Wu, Yi-HungHsu, Jia-Lien
口試日期:2020-07-30
學位類別:碩士
校院名稱:國立清華大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:英文
論文頁數:34
中文關鍵詞:加護病房再住院多模組機器學習深度學習
外文關鍵詞:Intensive care unitReadmissionMultimodalMachine learningDeep learning
相關次數:
  • 被引用被引用:0
  • 點閱點閱:289
  • 評分評分:
  • 下載下載:32
  • 收藏至我的研究室書目清單書目收藏:0
加護病房的支出正逐年上漲。為了要減少支出,醫護人員傾向讓病患提早離開加護病房。然而,這樣可能會使病患的狀況惡化,最後導致再住回加護病房。如果醫護人員可以知道再住院風險高的病患有哪些人的話,就可以避免那些人的再住院。另一方面,醫護人員可以讓再住院風險較低的患者離開,以降低醫院的費用。因此,加護病房再住院預測對病患的健康和減少加護病房的支出均有幫助。在這項研究中,我們提出了一個用於預測再住院風險的多模型架構。我們使用三種不同的模型來處理三種不同類型的資料,並結合三種模型的結果來得到每個病患再住院風險的最終預測結果。實驗結果顯示,我們提出的方法可以達到0.755的AUROC,證明它可以有效地預測每位病患的加護病房再住院風險。
The cost of intensive care unit (ICU) is increasing annually. For reducing cost, physicians tend to discharge patients from ICU at an early time. However, it may cause bad result of patient’s outcome, finally lead to readmission. Supposed that physicians could know certain patients that prone to readmission, their readmission could be avoided. In contrast, in order to reduce the cost, patients with low readmission risk could be discharged by physicians. Therefore, ICU readmission prediction is helpful for both the physical condition of patients and ICU cost. In this study, we propose a multimodal architecture to predict readmission risk. There are three different models to utilize three different types of data. We combine results of three models to make final prediction of each patient. Experimental results show that our proposed method can achieve up to 0.755 AUROC, prove that it can effectively predict ICU readmission risk for each patient.
Acknowledgement 1
摘要 2
Abstract 3
Table of Contents 4
List of Figures 5
List of Tables 6
1. Introduction 7
2. Related Work 11
2.1 Research on MIMIC-III 11
2.2 Multimodal 12
2.3 ICU Readmission Prediction 12
3. Preliminary 15
3.1 Task Description 15
3.2 Description of Dataset 15
3.3 Cohort Selection 17
3.4 Data Balancing 18
4. Method 19
4.1 Numerical Feature Extraction 19
4.2 Categorical Feature Extraction 20
4.3 Time Series Feature Extraction 20
4.4 Model Architecture 21
4.5 Sequential Forward Selection 23
5. Experiments 26
5.1 Implementation Details 26
5.2 Performance Evaluation for Three Models 26
1) Numerical Model 26
2) Categorical Model 26
3) Time Series Model 27
5.3 Final Prediction Performance 28
6. Conclusion 30
Reference 31
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