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449 results on '"Wu, Meiying"'

27. Epidemiology of concurrent extrapulmonary tuberculosis in inpatients with extrapulmonary tuberculosis lesions in China: a large-scale observational multi-centre investigation

Author

Kang, Wanli, Liu, Shengsheng, Du, Jian, Tang, Peijun, Chen, Hongyan, Liu, Jianxiong, Ma, Jinshan, Li, Mingwu, Qin, Jingmin, Shu, Wei, Zong, Peilan, Zhang, Yi, Dong, Yongkang, Yang, Zhiyi, Mei, Zaoxian, Deng, Qunyi, Wang, Pu, Han, Wenge, Yan, Xiaofeng, Chen, Ling, Zhao, Xinguo, Tan, Lei, Li, Fujian, Zheng, Chao, Liu, Hongwei, Li, Xinjie, A, Ertai, Du, Yingrong, Liu, Fenglin, Cui, Wenyu, Wang, Quanhong, Chen, Xiaohong, Han, Junfeng, Xie, Qingyao, Feng, Yanmei, Liu, Wenyu, Yang, Song, Zhang, Jianyong, Zheng, Jian, Chen, Dawei, Yao, Xiangyang, Ren, Tong, Li, Yan, Li, Yuanyuan, Wu, Lei, Song, Qiang, Shen, Xinghua, Zhang, Jian, Liu, Yuanyuan, Guo, Shuliang, Yan, Kun, Yang, Mei, Lei, Dan, Zhang, Yanli, Wu, Meiying, Li, Liang, and Tang, Shenjie

Published
2022
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29. Machine learning models for prediction of postoperative venous thromboembolism in gynecological malignant tumor patients.

Author

Lin, Bijuan, Chen, Fang, Wu, Meiying, Li, Chaojing, and Lin, Lanzhi

Subjects
THROMBOEMBOLISM risk factors, HYSTERECTOMY, RISK assessment, PREDICTION models, RECEIVER operating characteristic curves, VEINS, LOGISTIC regression analysis, DECISION making in clinical medicine, RETROSPECTIVE studies, DESCRIPTIVE statistics, AGE distribution, TREATMENT duration, SURGICAL therapeutics, FEMALE reproductive organ tumors, SURGICAL complications, THROMBOEMBOLISM, STATISTICS, MACHINE learning, LENGTH of stay in hospitals, TUMOR classification, BLOOD transfusion, DISEASE complications
Abstract

Aim: To identify risk factors that associated with the occurrence of venous thromboembolism (VTE) within 30 days after hysterectomy among gynecological malignant tumor patients, and to explore the value of machine learning (ML) models in VTE occurrence prediction. Methods: A total of 1087 patients between January 2019 and January 2022 with gynecological malignant tumors were included in this single‐center retrospective study and were randomly divided into the training dataset (n = 870) and the test dataset (n = 217). Univariate logistic regression analysis was used to identify risk factors that associated with the occurrence of postoperative VTE in the training dataset. Machine learning models (including decision tree (DT) model and logistic regression (LR) model) to predict the occurrence of postoperative VTE were constructed and internally validated. Results: The incidence of developing 30‐day postoperative VTE was 6.0% (65/1087). Age, previous VTE, length of stay (LOS), tumor stage, operative time, surgical approach, lymphadenectomy (LND), intraoperative blood transfusion and gynecologic Caprini (G‐Caprini) score were identified as risk factors for developing postoperative VTE in gynecological malignant tumor patients (p < 0.05). The AUCs of LR model and DT model for predicting VTE were 0.722 and 0.950, respectively. Conclusion: The ML models, especially the DT model, constructed in our study had excellent prediction value and shed light upon its further application in clinic practice. [ABSTRACT FROM AUTHOR]

Published
2024
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33. Drug Exposure and Susceptibility of Pyrazinamide Correlate with Treatment Response in Pyrazinamide-Susceptible Patients with Multidrug-Resistant Tuberculosis

Author

Dong, Shulan, primary, Shao, Ge, additional, Davies Forsman, Lina, additional, Wang, Sainan, additional, Wang, Shanshan, additional, Cao, Jiayi, additional, Bao, Ziwei, additional, Bruchfeld, Judith, additional, Alffenaar, Jan-Willem C., additional, Liu, Jia, additional, Hu, Yi, additional, and Wu, Meiying, additional

Published
2024
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41. The prevalence and risks of major comorbidities among inpatients with pulmonary tuberculosis in China from a gender and age perspective: a large-scale multicenter observational study

Author

Kang, Wanli, Du, Jian, Yang, Song, Yu, Jiajia, Chen, Hongyan, Liu, Jianxiong, Ma, Jinshan, Li, Mingwu, Qin, Jingmin, Shu, Wei, Zong, Peilan, Zhang, Yi, Dong, Yongkang, Yang, Zhiyi, Mei, Zaoxian, Deng, Qunyi, Wang, Pu, Han, Wenge, Wu, Meiying, Chen, Ling, Zhao, Xinguo, Tan, Lei, Li, Fujian, Zheng, Chao, Liu, Hongwei, Li, Xinjie, Ertai, A., Du, Yingrong, Liu, Fenglin, Cui, Wenyu, Wang, Quanhong, Chen, Xiaohong, Han, Junfeng, Xie, Qingyao, Feng, Yanmei, Liu, Wenyu, Tang, Peijun, Zhang, Jianyong, Zheng, Jian, Chen, Dawei, Yao, Xiangyang, Ren, Tong, Li, Yang, Li, Yuanyuan, Wu, Lei, Song, Qiang, Yang, Mei, Zhang, Jian, Liu, Yuanyuan, Guo, Shuliang, Yan, Kun, Shen, Xinghua, Lei, Dan, Zhang, Yangli, Yan, Xiaofeng, Li, Liang, and Tang, Shenjie

Published
2021
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43. What Is Necessary for Digital Transformation of Large Manufacturing Companies? A Necessary Condition Analysis.

Author

Zhang, Ziye, Wu, Meiying, and Yin, Jiajie

Abstract

Digital transformation is of significant importance to the sustainable development of manufacturing companies and the construction of the digital economy. However, this major change is often hindered by numerous complex antecedents. What are the key factors in the digital transformation of manufacturing companies, and what is their relative importance? Accordingly, this paper identifies the key factors for digital transformation in large manufacturing companies from the "Ability–Motivation–Opportunity" (AMO) perspective. This study uses a necessary condition analysis (NCA) to conduct a necessity causality study on data collected from 67 listed Chinese manufacturing companies between 2016 and 2020. The results show that the digital transformation of large manufacturing companies is influenced by four necessary conditions: managerial myopia, industry concentration (very large effect), dynamic capabilities, and industrial digitalization (large effect). Managerial myopia and industry concentration have a negative necessary impact on digital transformation. The types of conditions and the level of bottlenecks required at different stages of digital transformation vary significantly. This study reveals the necessary causal relationships between organizational abilities, motivation, external opportunities, and digital transformation, providing empirical evidence to promote the digital transformation practices of manufacturing companies. [ABSTRACT FROM AUTHOR]

Published
2024
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49. A nanocomposite hydrogel for co-delivery of multiple anti-biofilm therapeutics to enhance the treatment of bacterial biofilm-related infections

Author

Liang, Shu, primary, Xiao, Lingyun, additional, Fang, Yixuan, additional, Chen, Tian, additional, Xie, Yuan, additional, Peng, Zhangwen, additional, Wu, Meiying, additional, Liu, Yang, additional, Xie, Julin, additional, Nie, Yichu, additional, Zhao, Xizhe, additional, Deng, Yang, additional, Zhao, Chao, additional, and Mai, Yang, additional

Published
2023
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