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1,623 results on '"Du, Jin"'

11. Extrapolated cross-validation for randomized ensembles

Author

Du, Jin-Hong, Patil, Pratik, Roeder, Kathryn, and Kuchibhotla, Arun Kumar

Subjects
Statistics - Methodology, Statistics - Machine Learning
Abstract

Ensemble methods such as bagging and random forests are ubiquitous in various fields, from finance to genomics. Despite their prevalence, the question of the efficient tuning of ensemble parameters has received relatively little attention. This paper introduces a cross-validation method, ECV (Extrapolated Cross-Validation), for tuning the ensemble and subsample sizes in randomized ensembles. Our method builds on two primary ingredients: initial estimators for small ensemble sizes using out-of-bag errors and a novel risk extrapolation technique that leverages the structure of prediction risk decomposition. By establishing uniform consistency of our risk extrapolation technique over ensemble and subsample sizes, we show that ECV yields $\delta$-optimal (with respect to the oracle-tuned risk) ensembles for squared prediction risk. Our theory accommodates general ensemble predictors, only requires mild moment assumptions, and allows for high-dimensional regimes where the feature dimension grows with the sample size. As a practical case study, we employ ECV to predict surface protein abundances from gene expressions in single-cell multiomics using random forests. In comparison to sample-split cross-validation and $K$-fold cross-validation, ECV achieves higher accuracy avoiding sample splitting. At the same time, its computational cost is considerably lower owing to the use of the risk extrapolation technique. Additional numerical results validate the finite-sample accuracy of ECV for several common ensemble predictors under a computational constraint on the maximum ensemble size., Comment: Accepted by the Journal of Computational and Graphical Statistics

Published
2023
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12. Action Research on Constructing a Welfare Pluralism System for the Elderly with Dementia: Taking the Elderly Health Social Work Service of Beijing X Hospital as an Example

Author

DU Jin, ZHANG Hongyu, QIAO Yuchen, LIU Yifan, LI Luling

Subjects
cognition disorders, social workers, aged, welfare pluralism, action research, full-cycle support, Medicine
Abstract

In the context of population ageing, the number of elderly patients with dementia is increasing day by day. This disease seriously affects the quality of life of the elderly and causes huge burdens and pressures on families and society. At this stage, issues such as low level of attention at the national and social levels, weak ability of active screening and identification, high treatment and care costs, and fragmented social service system are prominent in the elderly dementia. It is imperative to actively explore the construction of a care system that covers the entire life cycle of dementia in the elderly, which is composed of multiple welfare subjects such as the government, community, family, market, and civil society organizations. Based on the practice of the full-cycle support project for elderly patients with dementia and their families in Beijing X Hospital, the research takes the theory of welfare pluralism as the framework, and constructs the implementation path and mechanism of the welfare pluralism care system for the elderly with dementia through action research. Based on the portrayal of the reality of the elderly with dementia, health social workers break through the shortcomings of the original medical social work service, such as the single service object, the confinement of the service place, the disconnection of the needs of patients with different disease cycles, and the poor effect of the resource link, etc., turn to the perspective of health social work to use the method of action research to try to break the situation from four directions: in the stage of early detection of dementia, we can clarify the responsibilities of service subjects and maximize service effectiveness; in the stage of early treatment of dementia, we can break down barriers between service subjects and realize the value of resources flow; in the stage of self-management of patients with dementia, we can mobilize the subjective initiative of service subjects to achieve positive transformation of target group; in the dementia friendly advocacy stage, we can respond to the internal needs of service subjects and achieve sustainable development of the care system.

Published
2024
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19. Primary Surgery Followed by Selective Chemoradiotherapy Versus Preoperative Chemoradiotherapy Followed by Surgery for Locally Advanced Rectal Cancer: A Randomized Clinical Trial

Author

Li, Jun, Hu, Ye-Ting, Liu, Cheng-Cheng, Wang, Liu-Hong, Ju, Hai-Xing, Huang, Xue-Feng, Chi, Pan, Du, Jin-Lin, Wang, Jian-Ping, Xiao, Yi, Lin, Guo-Le, Zhang, Wei, Zhao, Hong, Liu, Ming, Song, Yong-Mao, Xu, Dong, Wang, Jian-Wei, Sun, Li-Feng, Xie, Hai-Ting, Cao, Hong-Feng, Xiao, Qian, Wang, Jian, Wu, Qing-Bin, Li, De-Chuan, Dai, Sheng, Jiang, Wei-Zhong, Shen, Li, Yuan, Ying, Wang, Zi-Qiang, and Ding, Ke-Feng

Published
2024
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34. Preoperative prediction model for microvascular invasion in HBV-related intrahepatic cholangiocarcinoma

Author

Liang Yu, Mu-Gen Dai, Wen-Feng Lu, Dong-Dong Wang, Tai-Wei Ye, Fei-Qi Xu, Si-Yu Liu, Lei Liang, and Du-Jin Feng

Subjects
Intrahepatic cholangiocarcinoma, Microvascular invasion, Prediction model, Hepatitis B virus, Preoperative, Surgery, RD1-811
Abstract

Abstract Background and aims Preoperative prediction of microvascular invasion (MVI) using a noninvasive method remain unresolved, especially in HBV-related in intrahepatic cholangiocarcinoma (ICC). This study aimed to build and validate a preoperative prediction model for MVI in HBV-related ICC. Methods Patients with HBV-associated ICC undergoing curative surgical resection were identified. Univariate and multivariate logistic regression analyses were performed to determine the independent risk factors of MVI in the training cohort. Then, a prediction model was built by enrolling the independent risk factors. The predictive performance was validated by receiver operator characteristic curve (ROC) and calibration in the validation cohort. Results Consecutive 626 patients were identified and randomly divided into the training (418, 67%) and validation (208, 33%) cohorts. Multivariate analysis showed that TBIL, CA19-9, tumor size, tumor number, and preoperative image lymph node metastasis were independently associated with MVI. Then, a model was built by enrolling former fiver risk factors. In the validation cohort, the performance of this model showed good calibration. The area under the curve was 0.874 (95% CI: 0.765–0.894) and 0.729 (95%CI: 0.706–0.751) in the training and validation cohort, respectively. Decision curve analysis showed an obvious net benefit from the model. Conclusion Based on clinical data, an easy model was built for the preoperative prediction of MVI, which can assist clinicians in surgical decision-making and adjuvant therapy.

Published
2023
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35. Irregular Menstruation-Hirsutism-Infertility: Clinical Management of Non-Classic Congenital Adrenal Hyperplasia

Author

NIE Zhimei, DOU Jianxin, DU Jin, GU Weijun, LYU Zhaohui, DOU Jingtao, and MU Yiming

Subjects
nonclassical 21-hydroxylase deficiency, 17-hydroxyprogesterone, cosyntropin-stimulation test, Medicine
Abstract

A 38-year-old female presented with irregular menstruation and hirsutism that started at age of 16 and diagnosed with polycystic ovary syndrome at age of 29 with elevated testosterone. When treated with ethinestradiol cyproterone tablets, her menstruation returned to normal and androgen levels was not changed. At age of 38 she was referred to the hospital with infertility, a diagnosis of nonclassical 21-hydroxylase deficiency was confirmed using 17-hydroxyprogesterone, dehydroepiandrosterone-sulfate, a cosyntropin-stimulation test and genetic test. This case suggested that nonclassical congenital adrenal hyperplasia should be considered when a patient is presented with oligomenorrhea, hirsutism with hyperandrogenemia and infertility.

Published
2023
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