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Predictive modeling for postoperative delirium in elderly patients with abdominal malignancies using synthetic minority oversampling technique 被引量:3
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作者 Wen-Jing Hu Gang Bai +6 位作者 Yan Wang Dong-Mei Hong jin-hua jiang Jia-Xun Li Yin Hua Xin-Yu Wang Ying Chen 《World Journal of Gastrointestinal Oncology》 SCIE 2024年第4期1227-1235,共9页
BACKGROUND Postoperative delirium,particularly prevalent in elderly patients after abdominal cancer surgery,presents significant challenges in clinical management.AIM To develop a synthetic minority oversampling techn... BACKGROUND Postoperative delirium,particularly prevalent in elderly patients after abdominal cancer surgery,presents significant challenges in clinical management.AIM To develop a synthetic minority oversampling technique(SMOTE)-based model for predicting postoperative delirium in elderly abdominal cancer patients.METHODS In this retrospective cohort study,we analyzed data from 611 elderly patients who underwent abdominal malignant tumor surgery at our hospital between September 2020 and October 2022.The incidence of postoperative delirium was recorded for 7 d post-surgery.Patients were divided into delirium and non-delirium groups based on the occurrence of postoperative delirium or not.A multivariate logistic regression model was used to identify risk factors and develop a predictive model for postoperative delirium.The SMOTE technique was applied to enhance the model by oversampling the delirium cases.The model’s predictive accuracy was then validated.RESULTS In our study involving 611 elderly patients with abdominal malignant tumors,multivariate logistic regression analysis identified significant risk factors for postoperative delirium.These included the Charlson comorbidity index,American Society of Anesthesiologists classification,history of cerebrovascular disease,surgical duration,perioperative blood transfusion,and postoperative pain score.The incidence rate of postoperative delirium in our study was 22.91%.The original predictive model(P1)exhibited an area under the receiver operating characteristic curve of 0.862.In comparison,the SMOTE-based logistic early warning model(P2),which utilized the SMOTE oversampling algorithm,showed a slightly lower but comparable area under the curve of 0.856,suggesting no significant difference in performance between the two predictive approaches.CONCLUSION This study confirms that the SMOTE-enhanced predictive model for postoperative delirium in elderly abdominal tumor patients shows performance equivalent to that of traditional methods,effectively addressing data imbalance. 展开更多
关键词 Elderly patients Abdominal cancer Postoperative delirium Synthetic minority oversampling technique Predictive modeling Surgical outcomes
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Efficient processing of ordered XML twig pattern matching based on extended Dewey 被引量:1
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作者 jin-hua jiang Ke CHEN +2 位作者 Xiao-yan LI Gang CHEN Li-dan SHOU 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2009年第12期1769-1783,共15页
Finding all occurrences of a twig pattern is a core operation of extensible markup language (XML) query processing. Holistic twig join algorithms, which avoid a large number of intermediate results, represent the stat... Finding all occurrences of a twig pattern is a core operation of extensible markup language (XML) query processing. Holistic twig join algorithms, which avoid a large number of intermediate results, represent the state-of-the-art algorithms. However, ordered XML twig join is mentioned rarely in the literature and previous algorithms developed in attempts to solve the problem of ordered twig pattern (OTP) matching have poor performance. In this paper, we first propose a novel children linked stacks encoding scheme to represent compactly the partial ordered twig join results. Based on this encoding scheme and extended Dewey, we design a novel holistic OTP matching algorithm, called OTJFast, which needs only to access the labels of the leaf query nodes. Furthermore, we propose a new algorithm, named OTJFaster, incorporating three effective optimization rules to avoid unnecessary computations. This works well on available indices (such as B+-tree), skipping useless elements. Thus, not only is disk access reduced greatly, but also many unnecessary computations are avoided. Finally, our extensive experiments over both real and synthetic datasets indicate that our algorithms are superior to previous approaches. 展开更多
关键词 XML querying Ordered twig join Index Optimization
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