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Trend Analysis and Intervention Effect Starting Point Detection of COVID-19 Epidemics Using Recalibrated Time Series Models—Worldwide,2020

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China CDC weekly 2021年第20期3卷 417-422页

作者： Shu Li Chen Chen Shuyin Cao Kejia Hu Hao Lei Xiaolin Xu Qinchuan Wang Changzheng Yuan Sicong Wang Sisi Wang Junlin Jia Yuanqing Ye Xifeng Wu Center of Clinical Big Data and Analytics Second Affiliated Hospital and Department of Big Data Health Science School of Public HealthZhejiang University School of MedicineHangzhouZhejiangChina National Institute for data Science in Health and Medicine HangzhouZhejiang UniversityZhejiangChina Department of Surgical Oncology Affiliated Sir Run Run Shaw HospitalZhejiang University School of MedicineHangzhouZhejiangChina.

Objective:This study aimed to identify a model for short-term coronavirus disease 2019(COVID-19)trend prediction and intervention evaluation.Methods:We compared the autoregressive integrated moving average(ARIMA)model and Holt exponential smoothing(Holt)model on predicting the number of cumulative COVID-19 cases in China.Based on the mean absolute percentage error(MAPE)value,the optimal model was selected and further tested using data from the United States,Italy and Republic of Korea.The intervention effect starting time points and abnormal trend changes were detected by observing the pattern of differences between the predicted and real trends.Results:The recalibrated ARIMA model with a 5-day prediction time span has the best model performance with MAPEs ranged between 2%and 5%.The intervention effects started to show on February 7 in the mainland of China,March 5 in Republic of Korea and April 27 in Italy,but have not been detected in the US as of May 19.Temporary abnormal trends were detected in Korea and Italy,but the overall epidemic trends were stable since the effect starting points.Conclusion:The recalibrated ARIMA model can detect the intervention effects starting points and abnormal trend changes;thus to provide valuable information support for epidemic trend analysis and intervention evaluation.

关键词： starting absolute smoothing

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Influencing Factors of Healthy Aging Risk Assessed Using Biomarkers:A Life Course Perspective

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China CDC weekly 2024年第11期6卷 219-224,I0014-I0017页

作者： Cedric Zhang Bo Lua Yajie Gao Jinming Li Xingqi Cao Xinwei Lyu Yinuo Tu Shuyi Jin Zuyun Liu center for clinical big data and analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Healththe Key Laboratory of Intelligent Preventive Medicine of Zhejiang ProvinceZhejiang University School of MedicineHangzhou CityZhejiang ProvinceChina Institute of Epidemiology and Health Care University College LondonLondonUK College of Chemical and Biological Engineering Zhejiang UniversityHangzhou CityZhejiang ProvinceChina

Assessing individual risks of healthy aging using biomarkers and identifying associated factors have become important areas of research.In this study,we conducted a literature review of relevant publications between 2018 and 2023 in both Chinese and English databases.Previous studies have predominantly used single biomarkers,such as C-reactive protein,or focused on specific life course stages and factors such as socioeconomic status,mental health,educational levels,and unhealthy lifestyles.By summarizing the progress in this field,our study provides valuable insights and future directions for promoting healthy aging from a life course perspective.

关键词： such insight directions

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Temperature change between neighboring days and hospital admissions in China

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Advances in Climate Change Research 2023年第6期14卷 847-855页

作者： Yu-Shu HUANG He-Jia SONG Yi-Bin CHENG Shilu TONG Rui ZHANG Xiao-Yuan YAO Yong-Hong LI China CDC Key Laboratory of Environment and Population Health National Institute of Environmental HealthChinese Center for Disease Control and PreventionBeijing 100021China Deparment of big data in Health Science School of Public Health and Centerof Clinical Big Data and Analytics of the Second Affiliated HospialZhejiang University School of MedicineHangzhou 310058China Jiaxing center for Disease Control and Prevention Jiaxing 314050China Instinurte of Environmenr and Population Health School of Public HealrhAnhui Medical UniversityHefei 230032China School of Public Health and Social Work Queensland University of TechnologyBrisbane 4006Australia Information center Chinese Center for Disease Control and PreventionBeiing 102206China

The short-term temperature fluctuation caused by global climate change is one of the risk factors affecting public health.Exploring the association between temperature fluctuation and diseases,which has received relatively limited research attention,can contribute to enhancing awareness of health risks and taking timely measures for health risk alert and management.Therefore,this study aims to investigate the relationship between temperature change between neighboring days(TCN)and hospitalizations,identify diseases sensitive to extreme TCN,and evaluate the related disease burden.We collected meteorological and hospitalization data from 2014 to 2019 in 23 sites of China to explore the impact of TCN on hospitalizations.We first quantified site-specific associations between TCN and hospitalizations and then conducted meta-analysis to pool the results,to assess the relative risk of extreme TCN for susceptible diseases,and to estimate the related disease burden attributed to TCN.Stratified analyses were undertaken by age,sex,and disease type.Results showed that all-cause hospital admission was significantly linked to TCN.A negative TCN(below-1.9℃)in the cool season and a positive TCN(above 1.0℃)in the warm season increased the risk of hospitalization.People aged 15-64 years,men,and patients with musculoskeletal system or connective tissue diseases were more sensitive to extremely negative TCN during the cool season.People aged over 65 years,men,and patients with respiratory diseases were more sensitive to extremely positive TCN during the warm season.The attributable fraction to all-cause hospitalization from negative TCN in the cool season was 2.05%(95%CI:-0.90%,4.53%)and from positive TCN in the warm season was 5.79%(95%CI:2.98%,8.31%).Circulatory diseases in the cool season and respiratory diseases in the warm season had the highest disease burden.Our findings indicate that awareness of TCN and its health risks should be promoted and evidence-informed policies are needed to reduce the risk of TCN.

关键词： Temperature change between neighboring days Morbidity Hospital admissions Sensitive diseases Vulnerable populations Disease burden

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Massive data clustering by multi-scale psychological observations

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National Science Review 2022年第2期9卷 43-51页

作者： Shusen Yang Liwen Zhang Chen Xu Hanqiao Yu Jianqing Fan Zongben Xu National Engineering Laboratory of big data analytics Xi'an Jiaotong University Department of Mathematics and Statistics University of Ottawa center for Statistics and Machine Learning Princeton University Industrial Artificial Intelligent center Pazhou Laboratory

Clustering is the discovery of latent group structure in data and is a fundamental problem in artificial intelligence,and a vital procedure in data-driven scientific research over all disciplines.Yet,existing methods have various limitations,especially we ak cognitive interpretability and poor computational scalability,when it comes to clustering massive datasets that are increasingly available in all domains.Here,by simulating the multi-scale cognitive observation process of humans,we design a scalable algorithm to detect clusters hierarchically hidden in massive datasets.The observation scale changes,following the Weber-Fechner law to capture the gradually emerging meaningful grouping structure.We validated our approach in real datasets with up to a billion records and 2000 dimensions,including taxi trajectories,single-cell gene expressions,face images,computer logs and audios.Our approach outperformed popular methods in usability,efficiency,effectiveness and robustness across different domains.

关键词： massive data clustering psychological observation Weber–Fechner law cognitive interpretability computational scalability

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Effects of dietary oligopeptide(iron chelate) on pregnant rats with iron deficiency anemia and offspring development

Effects of dietary oligopeptide(iron chelate) on pregnant ra...

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中国营养学会第十五届全国营养科学大会

作者： Wenfei Pan He Gao Xiaoling Ying Jili Chen Feiyan Wu Sasa Xie Min Yang Department of Nutrition and food hygiene School of Public Health and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital Zhejiang University School of Medicine Linping Campus The Second Affiliated Hospital of Zhejiang University School of Medicine

Iron deficiency anemia(IDA) is a serious publichealth problem worldwide, and traditional iron supplements such as ferroussulfate have many adverse effects. This study was to systematically evaluatethe effects of dietary oligopeptide(iron chelate) supplementation on IDApregnant rats and offspring, and to provide a basis for the development of new dietaryoligopeptide(iron chelate) supplements. Methods The IDA model was established using SPF Sprague Dawley(SD) female rats fed with low iron diet and divided into seven groups(6-9 rats per group) according to hemoglobin(Hb) and body weight: IDA model group atiron dose of 4 ppm(ID), high dose of ferrous sulfate group at iron dose of 400 ppm(H-FeSO4),low dose of ferrous sulfate group at iron dose of 40 ppm(L-FeSO4),marine fish oligopeptide chelated iron group at iron dose of 400 ppm(MCOP-Fe),marine fish oligopeptide group at iron dose of 40 ppm(MOP), whey proteinoligopeptide chelated iron group at iron dose of 400 ppm(WPPFe) and wheyprotein oligopeptide group at iron dose of 40 ppm(WPOP). Rats in each groupwere fed with the corresponding special diet during pregnancy until the day ofdelivery. Hemoglobin(Hb), ironmetabolism, liver oxidative stress, tumor necrosis factor α(TNF-α) and development of offspring were investigated. Results 1) After iron supplementation, the Hb of IDA pregnant rats in all interventiongroups was higher than that of the ID group(all P<0.05), and the MCOP-Fe and WPP-Fe groups had more advantageousability to restoring Hb. 2) Among the ironmetabolism-related indexes, serum iron(Fe), serum ferritin(Fer), total ironbinding capacity(TiBC) and liver tissue iron content were significantly higherin the WPP-Fe group than the ID group(P<0.05). 3) The liver malondialdehyde(MDA) content inthe MCOP-Fe and WPP-Fe group was significantly higher than the ID group(P<0.05), while the superoxidedismutase(SOD) content in the WPP-Fe and WPOP group showed a tendency to besuperior to other groups. 4) TNF-αconcentration was notably higher in the ID group(P<0.05) and was n

关键词： Iron deficiency anemia Pregnant rats Dietary oligopeptide（iron chelate）

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Causal Association of Cardiac Function by Magnetic Resonance Imaging with Frailty Index:A Mendelian Randomization Study

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Phenomics 2022年第6期2卷 430-437页

作者： Hui Zhang Meng Hao Zixin Hu Yi Li Xiaoxi Hu Xiaoyan Jiang Zuyun Liu Xuehui Sun Xiaofeng Wang Human Phenome Institute Zhangjiang Fudan International Innovation CenterFudan UniversityShanghai 200433China Fudan University-the People’s Hospital of Rugao Joint Research Institute of Longevity and Ageing Rugao 226599JiangsuChina Key Laboratory of Arrhythmias of the Ministry of Education of China Tongji University School of MedicineShanghai 200092China center for clinical big data and analytics Second Affiliated Hospital and Department of Big Data in Health ScienceSchool of Public HealthZhejiang University School of MedicineHangzhou 310030ZhejiangChina

Owing to the susceptibility of conventional observational studies to confounding factors and reverse causation,the causal association between cardiac function and frailty is unclear.We aimed to investigate whether cardiac function has causal effects on frailty.In this study,a two-sample Mendelian randomization(MR)study was conducted using genetic variants associated with cardiac function assessed by magnetic resonance imaging phenotypes as instrumental variables.Genetic variants asso-ciated with cardiac function by magnetic resonance imaging(including seven cardiac function phenotypes)and the frailty index(FI)were obtained from two large genome-wide association studies.MR estimates from each genetic instrument were combined using inverse variance weighted(IVW),weighted median,and MR‒Egger regression methods.We found that the increase in genetically determined stroke volume(beta-0.13,95%CI-0.16 to-0.10,p=1.39E-6),rather than other cardiac phenotypes,was associated with lower FI in MR analysis of IVW after Bonferroni correction.Sensitivity analyses examining potential bias caused by pleiotropy or reverse causality revealed similar findings(e.g.,intercept[SE],-0.008[0.011],p=0.47 by MR‒Egger intercept test).The leave-one-out analysis indicated that the association was not driven by single nucleotide polymorphisms.No evidence of heterogeneity was found among the genetic variants(e.g.,MR‒Egger:Q statistic=14.4,p=0.156).In conclusion,we provided evidence that improved cardiac function could contribute to reducing FI.These findings support the hypothesis that enhancing cardiac function could be an effective prevention strategy for frailty.

关键词： Cardiac function Frailty Mendelian randomization Genetics

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Unlabeled data driven cost-sensitive inverse projection sparse representation-based classification with 1/2 regularization

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Science China(Information Sciences) 2022年第8期65卷 39-56页

作者： Xiaohui YANG Zheng WANG Jian SUN Zongben XU Henan Engineering Research center for Artificial Intelligence Theory and Algorithms School of Mathematics and StatisticsHenan University National Engineering Laboratory for big data analytics School of Mathematics and StatisticsXi'an Jiaotong University

Sparse representation-based classification(SRC) has been widely used because it just relies on simple linear regression ideas to do classification, and it does not need learning. However, the performance of SRC is limited by needing sufficient labeled samples per class and the sensitivity to class imbalance. For tackling these problems, an improved SRC model is constructed in this paper. For alleviating the problem of insufficient labeled samples, an unlabeled data-driven inverse projection sparse representation-based classification model is constructed to achieve effective and stable representation and recognition results. The L1/2 and S1/2 regularizations are introduced to capture the sparsity of 1-D and 2-D, and to make the model have good statistical properties. Moreover, the cost-sensitive strategy is integrated into the model’s classification criteria to improve the imbalance of class distribution adaptively, especially for multiclass imbalanced data.A solver utilizing the mixed Gauss-Seidel and Jacobian ADMM algorithm is developed to obtain the approximate solution. Experiments on common public test databases show that the proposed model achieves competitive results compared with the latest published results and some deep-learning algorithms.

关键词： unlabeled-data driven 1/2 regularization cost-sensitive inverse projection sparse representation-based classification

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Body mass index and survival after cancer diagnosis:A pan-cancer cohort study of 114430 patients with cancer

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The Innovation 2022年第6期3卷 159-165页

作者： Huakang Tu Jennifer LMcQuade Michael ADavies Maosheng Huang Kunlin Xie Yuanqing Ye Wong-Ho Chow Alma Rodriguez Xifeng Wu Department of big data in Health Science School of Public Health Center of Clinical Big Data and Analytics of The Second Affiliated HospitalZhejiang University School of MedicineHangzhou 310058China Department of Epidemiology The University of Texas MD Anderson Cancer CenterHoustonTX 77030USA The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province Hangzhou 310058China Department of Melanoma Medical Oncology The University of Texas MD Anderson Cancer CenterHoustonTX 77030USA Department of Liver Surgery&Liver Transplantation West China HospitalSichuan UniversityChengdu 610065China Department of Lymphoma/Myeloma The University of Texas MD Anderson Cancer CenterHoustonTX 77030USA Cancer center Zhejiang UniversityHangzhou 310058China

The recommendation encouraging patients with cancer to keep a normal body mass index(BMI)is largely extrapolated from data on risk of developing cancer.We tested the prospective association between peri-diagnostic(within 1 year post-diagnosis)BMI and all-cause mortality in patients with incident cancers.During 7.2 years of follow-up,42%(48,340)of the 114430 patients with cancer died.Spline analysis revealed that compared with a BMI of 22.5,a BMI lower than 22.5 was associated with increased risk of all-cause mortality across 24 cancer types.A BMI higher than 22.5 was associated with reduced all-cause mortality,while a non-linear association was observed;the lowest risk was found at a BMI of 29.6–34.2,and the risk started to return to and above unity at very high BMI values.The reduced mortality risk of high BMI was observed in 23 of 24 cancer types and maintained after attempts to remove potential selection bias,confounding by smoking and comorbidities,and reserve causality.

关键词： patients mortality diagnosis

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Reverse-Nearest-Neighbor-Based Clustering by Fast Search and Find of Density Peaks

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Chinese Journal of Electronics 2023年第6期32卷 1341-1354页

作者： ZHANG Chunhao XIE Bin ZHANG Yiran College of Computer and Cyber Security Hebei Normal University Hebei Provincial Engineering Research center for Supply Chain big data analytics and data Security Hebei Normal University Hebei Provincial Key Laboratory of Network and Information Security Hebei Normal University

Clustering by fast search and find of density peaks(CFSFDP) has the advantages of a novel idea, easy implementation, and efficient clustering. It has been widely recognized in various fields since it was proposed in Science in 2014. The CFSFDP algorithm also has certain limitations, such as non-unified sample density metrics defined by cutoff distance, the domino effect for the assignment of remaining samples triggered by unstable assignment strategy, and the phenomenon of picking wrong density peaks as cluster centers. We propose reverse-nearest-neighbor-based clustering by fast search and find of density peaks(RNN-CFSFDP) to avoid these shortcomings. We redesign and unify the sample density metric by introducing reverse nearest neighbor. The newly defined local density metric and the K-nearest neighbors of each sample are combined to make the assignment process more robust and alleviate the domino effect. A cluster fusion algorithm is proposed, which further alleviates the domino effect and effectively avoids the phenomenon of picking wrong density peaks as cluster centers. Experimental results on publicly available synthetic data sets and real-world data sets show that in most cases, the proposed algorithm is superior to or at least equivalent to the comparative methods in clustering performance. The proposed algorithm works better on manifold data sets and uneven density data sets.

关键词： Density peaks Reverse nearest neighbor Clustering Cluster fusion

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Privacy-preserving deep learning techniques for wearable sensor-based big data applications

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Virtual Reality & Intelligent Hardware 2022年第3期4卷 210-222页

作者： Rafik HAMZA Minh-Son DAO Integrated big data Research center|NICT-National Institute of Information and Communications Technology TokyoJapan

Wearable technologies have the potential to become a valuable influence on human daily life where they may enable observing the world in new ways,including,for example,using augmented reality(AR)applications.Wearable technology uses electronic devices that may be carried as accessories,clothes,or even embedded in the user's body.Although the potential benefits of smart wearables are numerous,their extensive and continual usage creates several privacy concerns and tricky information security challenges.In this paper,we present a comprehensive survey of recent privacy-preserving big data analytics applications based on wearable sensors.We highlight the fundamental features of security and privacy for wearable device applications.Then,we examine the utilization of deep learning algorithms with cryptography and determine their usability for wearable sensors.We also present a case study on privacy-preserving machine learning techniques.Herein,we theoretically and empirically evaluate the privacy-preserving deep learning framework's performance.We explain the implementation details of a case study of a secure prediction service using the convolutional neural network(CNN)model and the Cheon-Kim-Kim-Song(CHKS)homomorphic encryption algorithm.Finally,we explore the obstacles and gaps in the deployment of practical real-world applications.Following a comprehensive overview,we identify the most important obstacles that must be overcome and discuss some interesting future research directions.

关键词： Wearable technology Augmented reality Privacy-preserving Deep learning big data Secure prediction service

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