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    学术成果丨基地重大项目研究成果（四）

    2025-01-28

    为了利用数据分析、数字技术和信息技术来提升全民健康水平、优化医疗资源配置👩🏻‍🍳、推动精准医疗发展，本基地重大项目“数字健康的统计测度与分析”基于大数据、人工智能和健康信息系统等方法和工具在疾病预防、健康管理、治疗方案优化等方面进行了深入研究🎓，涉及复杂生存分析💲、医学高维数据、疾病预测模型、健康风险评估及因果推断等关键问题🤘。以下是项目组近期在健康领域取得的一些研究成果🦪🏌🏼：

    1. Zhan Z, Liu Z, Lin C, Yi D, Liu J, Yang Y. Censored C-learning for dynamic treatment regime in colorectal cancer study. The Annals of Applied Statistics. 2025. Online.

    2. 曹毓文,梅好,孙佳仪,胡炯宇.基于健康保险数据的医疗费用共病网络分析及深度学习预测.重庆医学, 2024(24).

    3. Luo S, Li W, Miao W, He Y. Identification and estimation of causal effects in the presence of confounded principal strata. Statistics in Medicine. 2024;43(22):4372-87.

    4. Zhang J, Chen Z, Yang Y, Xu W. Variable importance based interaction modelling with an application on initial spread of COVID-19 in China. Journal of the Royal Statistical Society Series C: Applied Statistics. 2024;73(5):1134-54.

    5. 林存洁,熊照,李扬.先验中介分析及其应用.系统科学与数学, 2024; 44(7):2122-2145.

    6. Chen Y, Zhang Q, Ma S, Fang K. Heterogeneity-aware Clustered Distributed Learning for Multi-source Data Analysis. Journal of Machine Learning Research. 2024;25(211):1-60.

    7. Wang F, Jia K, Li Y. Integrative deep learning with prior assisted feature selection. Statistics in Medicine. 2024;43(20):3792-814.

    8. Chen Y, Chang X, Zhang B, Huang H. Efficient and effective calibration of numerical model outputs using hierarchical dynamic models. The Annals of Applied Statistics. 2024;18(2):1064-89.

    9. Yang H, Qin Y, Li Y, Hu F. Sequential covariate-adjusted randomization via hierarchically minimizing Mahalanobis distance and marginal imbalance. Biometrics. 2024;80(2):ujae047. 

    10.  Xia J, Zhang Z, Zhang J. Doubly robust estimation of optimal treatment regimes for survival data using an instrumental variable. Statistics and Computing. 2024;34(3):96.

    11.  Wang Y, Deng H, Gao S, Li T, Wang F. A Fresh Perspective on Examining Population Emotional Well-Being Trends by Internet Search Engine: An Emerging Composite Anxiety and Depression Index. International Journal of Environmental Research and Public Health. 2024;21(2):202. 

    12.  Zhang Q, Wang F, Feng H, Xing J, Zhu S, Zhang H, Li Y, Wei W, Zhang S. Modifiable risk factors for esophageal cancer in endoscopic screening population: A modeling study. Chinese Medical Journal. 2024;137(03):350-2.

    论文题目与摘要

    1. Zhan Z, Liu Z, Lin C, Yi D, Liu J, Yang Y. Censored C-learning for dynamic treatment regime in colorectal cancer study. The Annals of Applied Statistics. 2025. Online.

    1.png

    2. 曹毓文,梅好,孙佳仪,胡炯宇.基于健康保险数据的医疗费用共病网络分析及深度学习预测.重庆医学, 2024(24).

    https://www.cnki.com.cn/Article/CJFDTotal-CQYX202424002.htm

    2.png

    3. Luo S, Li W, Miao W, He Y. Identification and estimation of causal effects in the presence of confounded principal strata. Statistics in Medicine. 2024;43(22):4372-87.

    https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.10175

    3.png

    4 Zhang J, Chen Z, Yang Y, Xu W. Variable importance based interaction modelling with an application on initial spread of COVID-19 in China. Journal of the Royal Statistical Society Series C: Applied Statistics. 2024;73(5):1134-54.

    https://doi.org/10.1093/jrsssc/qlae029

    4.png

    5. 林存洁,熊照,李扬.先验中介分析及其应用.系统科学与数学, 2024; 44(7):2122-2145.

    https://sysmath.cjoe.ac.cn/jweb_xtkxysx/CN/10.12341/jssms23406

    5.png

    6. Chen Y, Zhang Q, Ma S, Fang K. Heterogeneity-aware Clustered Distributed Learning for Multi-source Data Analysis. Journal of Machine Learning Research. 2024;25(211):1-60.

    https://www.jmlr.org/papers/v25/23-0059.html

    6.png

    7. Wang F, Jia K, Li Y. Integrative deep learning with prior assisted feature selection. Statistics in Medicine. 2024;43(20):3792-814.

    https://doi.org/10.1002/sim.10148

    7.png

    8. Chen Y, Chang X, Zhang B, Huang H. Efficient and effective calibration of numerical model outputs using hierarchical dynamic models. The Annals of Applied Statistics. 2024;18(2):1064-89.

    https://doi.org/10.1214/23-AOAS1823

    8.png

    9. Yang H, Qin Y, Li Y, Hu F. Sequential covariate-adjusted randomization via hierarchically minimizing Mahalanobis distance and marginal imbalance. Biometrics. 2024;80(2):ujae047. 

    https://doi.org/10.1093/biomtc/ujae047

    9.png

    10. Xia J, Zhang Z, Zhang J. Doubly robust estimation of optimal treatment regimes for survival data using an instrumental variable. Statistics and Computing. 2024;34(3):96. 

    https://link.springer.com/article/10.1007/s11222-024-10407-7

    10.png

    11. Wang Y, Deng H, Gao S, Li T, Wang F. A Fresh Perspective on Examining Population Emotional Well-Being Trends by Internet Search Engine: An Emerging Composite Anxiety and Depression Index. International Journal of Environmental Research and Public Health. 2024;21(2):202. 

    https://doi.org/10.3390/ijerph21020202

    11.png

    12. Zhang Q, Wang F, Feng H, Xing J, Zhu S, Zhang H, Li Y, Wei W, Zhang S. Modifiable risk factors for esophageal cancer in endoscopic screening population: A modeling study. Chinese Medical Journal. 2024;137(03):350-2.

    https://mednexus.org/doi/full/10.1097/CM9.0000000000002878

    12.png


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