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Machine learning-based prediction model for postoperative delirium in non-cardiac surgery
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 박, 래웅 | - |
| dc.contributor.author | 박, 정찬 | - |
| dc.date.accessioned | 2023-11-16T05:44:02Z | - |
| dc.date.available | 2023-11-16T05:44:02Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.uri | http://repository.ajou.ac.kr/handle/201003/26884 | - |
| dc.language.iso | en | - |
| dc.title | Machine learning-based prediction model for postoperative delirium in non-cardiac surgery | - |
| dc.title.alternative | 머신 러닝을 이용한 비심장수술 후 섬망 예측 모델:임상데이터 웨어하우스(CDW)에서 추출한 레지스트리 | - |
| dc.type | Thesis | - |
| dc.identifier.url | http://dcoll.ajou.ac.kr:9080/dcollection/jsp/common/DcLoOrgPer.jsp?sItemId=000000032836 | - |
| dc.subject.keyword | delirium | - |
| dc.subject.keyword | machine learning | - |
| dc.subject.keyword | predicting model | - |
| dc.subject.keyword | XGB | - |
| dc.subject.keyword | 섬망 | - |
| dc.subject.keyword | 기계학습 | - |
| dc.subject.keyword | 예측모델 | - |
| dc.description.degree | Doctor | - |
| dc.contributor.department | 대학원 의학과 | - |
| dc.contributor.affiliatedAuthor | 박, 정찬 | - |
| dc.date.awarded | 2023 | - |
| dc.type.local | Theses | - |
| dc.citation.date | 2023 | - |
| dc.embargo.liftdate | 9999-12-31 | - |
| dc.embargo.terms | 9999-12-31 | - |
| dc.description.tableOfContents | I. Introduction 1
A. Background and necessity of the study 1 1. Importance of Delirium after Surgery 1 2. Understanding Mechanism of Postoperative Delirium 5 3. Previous studies on Postoperative Delirium 11 B. Rational and Necessity of the Study 22 1. Pharmaco-economic Reasons 22 2. Clinical implication of prediction of postoperative delirium 23 C. Purpose of the study 27 II. Methods 28 A. Study population & Data Curation 30 B. Values of Da Features of Dataset ta Frame 33 C. Definition and Study Endpoints 37 D. Prediction model development 39 E. Model evaluation 50 F. Sub-analysis of internal validation dataset and external validation 54 G. Statistical Analysis 57 III. Result 61 A. Patient Characteristics 61 B. Development of prediction model 68 C. Development of prediction model with the entire variables 71 D. Development of prediction model using selected variables 74 E. Publicly accessible delirium prediction model 78 F. External Validation of Prediction Model 81 G. Clinical Usefulness of Prediction Models 83 H. Calibration of Prediction Model 89 IV. Discussion 92 A. The main finding of the study 92 B. Methodologic considerations to build models 95 C. The clinical implication of the study 98 D. The clinical strength of the study 100 E. Clinical suitability of the results 102 F. Clinical usefulness of the prediction model 105 G. Analysis of model performance considering the asymmetry of data 107 H. Limitations of the study 110 V. Conclusion 115 References 116 | - |
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