Journal of data science : JDS最新文献_第6页

Editorial: Data Science Meets Social Sciences 社论:数据科学遇上社会科学

Journal of data science : JDS Pub Date : 2022-01-01 DOI: 10.6339/22-jds203edi

E. Erosheva, Shahryar Minhas, Gongjun Xu, Ran Xu

引用次数: 0

An Effective Tensor Regression with Latent Sparse Regularization 具有隐稀疏正则化的有效张量回归

Journal of data science : JDS Pub Date : 2022-01-01 DOI: 10.6339/22-jds1048

Ko-Shin Chen, Tingyang Xu, Guannan Liang, Qianqian Tong, Minghu Song, J. Bi

{"title":"An Effective Tensor Regression with Latent Sparse Regularization","authors":"Ko-Shin Chen, Tingyang Xu, Guannan Liang, Qianqian Tong, Minghu Song, J. Bi","doi":"10.6339/22-jds1048","DOIUrl":"https://doi.org/10.6339/22-jds1048","url":null,"abstract":"As data acquisition technologies advance, longitudinal analysis is facing challenges of exploring complex feature patterns from high-dimensional data and modeling potential temporally lagged effects of features on a response. We propose a tensor-based model to analyze multidimensional data. It simultaneously discovers patterns in features and reveals whether features observed at past time points have impact on current outcomes. The model coefficient, a k-mode tensor, is decomposed into a summation of k tensors of the same dimension. We introduce a so-called latent F-1 norm that can be applied to the coefficient tensor to performed structured selection of features. Specifically, features will be selected along each mode of the tensor. The proposed model takes into account within-subject correlations by employing a tensor-based quadratic inference function. An asymptotic analysis shows that our model can identify true support when the sample size approaches to infinity. To solve the corresponding optimization problem, we develop a linearized block coordinate descent algorithm and prove its convergence for a fixed sample size. Computational results on synthetic datasets and real-life fMRI and EEG datasets demonstrate the superior performance of the proposed approach over existing techniques.","PeriodicalId":73699,"journal":{"name":"Journal of data science : JDS","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71320174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Does Aging Make Us Grittier? Disentangling the Age and Generation Effect on Passion and Perseverance 衰老会让我们变得更坚强吗?拆解年龄和世代对激情和毅力的影响

Journal of data science : JDS Pub Date : 2022-01-01 DOI: 10.6339/22-jds1041

S. Sanders, Nuwan Indika Millagaha Gedara, Bhavneet Walia, C. Boudreaux, M. Silverstein

{"title":"Does Aging Make Us Grittier? Disentangling the Age and Generation Effect on Passion and Perseverance","authors":"S. Sanders, Nuwan Indika Millagaha Gedara, Bhavneet Walia, C. Boudreaux, M. Silverstein","doi":"10.6339/22-jds1041","DOIUrl":"https://doi.org/10.6339/22-jds1041","url":null,"abstract":"Defined as perseverance and passion for long term goals, grit represents an important psychological skill toward goal-attainment in academic and less-stylized settings. An outstanding issue of primary importance is whether age affects grit, ceteris paribus. The 12-item Grit-O Scale and the 8-item Grit-S Scale—from which grit scores are calculated—have not existed for a long period of time. Therefore, Duckworth (2016, p. 37) states in her book, Grit: The Power and Passion of Perseverance, that “we need a different kind of study” to distinguish between rival explanations that either generational cohort or age are more important in explaining variation in grit across individuals. Despite this clear data constraint, we obtain a glimpse into the future in the present study by using a within and between generational cohort age difference-in-difference approach. By specifying generation as a categorical variable and age-in-generation as a count variable in the same regression specifications, we are able to account for the effects of variation in age and generation simultaneously, while avoiding problems of multicollinearity that would hinder post-regression statistical inference. We conclude robust, significant evidence that the negative-parabolic shape of the grit-age profile is driven by generational variation and not by age variation. Our findings suggest that, absent a grit-mindset intervention, individual-level grit may be persistent over time.","PeriodicalId":73699,"journal":{"name":"Journal of data science : JDS","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71320247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Do Americans Think the Digital Economy is Fair? Using Supervised Learning to Explore Evaluations of Predictive Automation 美国人认为数字经济公平吗?使用监督学习探索预测自动化的评估

Journal of data science : JDS Pub Date : 2022-01-01 DOI: 10.6339/22-jds1053

E. Lehoucq

引用次数: 1

High-Dimensional Nonlinear Spatio-Temporal Filtering by Compressing Hierarchical Sparse Cholesky Factors 压缩分层稀疏Cholesky因子的高维非线性时空滤波

Journal of data science : JDS Pub Date : 2022-01-01 DOI: 10.6339/22-jds1071

Anirban Chakraborty, M. Katzfuss

引用次数: 1

Supervised Spatial Regionalization using the Karhunen-Loève Expansion and Minimum Spanning Trees 基于karhunen - lo<e:1>展开和最小生成树的监督空间区划

Journal of data science : JDS Pub Date : 2022-01-01 DOI: 10.6339/22-jds1077

Ranadeep Daw, C. Wikle

引用次数: 2

On the Use of Deep Neural Networks for Large-Scale Spatial Prediction 深度神经网络在大尺度空间预测中的应用

Journal of data science : JDS Pub Date : 2022-01-01 DOI: 10.6339/22-jds1070

Skyler Gray, Matthew J. Heaton, D. Bolintineanu, A. Olson

引用次数: 2

Integration of Social Determinants of Health Data into the Largest, Not-for-Profit Health System in South Florida 整合健康数据的社会决定因素到最大的，非营利性的卫生系统在南佛罗里达州

Journal of data science : JDS Pub Date : 2022-01-01 DOI: 10.6339/22-jds1063

Lourdes M. Rojas, Gregory L. Vincent, D. Parris

{"title":"Integration of Social Determinants of Health Data into the Largest, Not-for-Profit Health System in South Florida","authors":"Lourdes M. Rojas, Gregory L. Vincent, D. Parris","doi":"10.6339/22-jds1063","DOIUrl":"https://doi.org/10.6339/22-jds1063","url":null,"abstract":"Social determinants of health (SDOH) are the conditions in which people are born, grow, work, and live. Although evidence suggests that SDOH influence a range of health outcomes, health systems lack the infrastructure to access and act upon this information. The purpose of this manuscript is to explain the methodology that a health system used to: 1) identify and integrate publicly available SDOH data into the health systems’ Data Warehouse, 2) integrate a HIPAA compliant geocoding software (via DeGAUSS), and 3) visualize data to inform SDOH projects (via Tableau). First, authors engaged key stakeholders across the health system to convey the implications of SDOH data for our patient population and identify variables of interest. As a result, fourteen publicly available data sets, accounting for >30,800 variables representing national, state, county, and census tract information over 2016–2019, were cleaned and integrated into our Data Warehouse. To pilot the data visualization, we created county and census tract level maps for our service areas and plotted common SDOH metrics (e.g., income, education, insurance status, etc.). This practical, methodological integration of SDOH data at a large health system demonstrated feasibility. Ultimately, we will repeat this process system wide to further understand the risk burden in our patient population and improve our prediction models – allowing us to become better partners with our community.","PeriodicalId":73699,"journal":{"name":"Journal of data science : JDS","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71320606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Active Data Science for Improving Clinical Risk Prediction 积极的数据科学改善临床风险预测

Journal of data science : JDS Pub Date : 2022-01-01 DOI: 10.6339/22-jds1078

D. Ankerst, Matthias Neumair

{"title":"Active Data Science for Improving Clinical Risk Prediction","authors":"D. Ankerst, Matthias Neumair","doi":"10.6339/22-jds1078","DOIUrl":"https://doi.org/10.6339/22-jds1078","url":null,"abstract":"Clinical risk prediction models are commonly developed in a post-hoc and passive fashion, capitalizing on convenient data from completed clinical trials or retrospective cohorts. Impacts of the models often end at their publication rather than with the patients. The field of clinical risk prediction is rapidly improving in a progressively more transparent data science era. Based on collective experience over the past decade by the Prostate Biopsy Collaborative Group (PBCG), this paper proposes the following four data science-driven strategies for improving clinical risk prediction to the benefit of clinical practice and research. The first proposed strategy is to actively design prospective data collection, monitoring, analysis and validation of risk tools following the same standards as for clinical trials in order to elevate the quality of training data. The second suggestion is to make risk tools and model formulas available online. User-friendly risk tools will bring quantitative information to patients and their clinicians for improved knowledge-based decision-making. As past experience testifies, online tools expedite independent validation, providing helpful information as to whether the tools are generalizable to new populations. The third proposal is to dynamically update and localize risk tools to adapt to changing demographic and clinical landscapes. The fourth strategy is to accommodate systematic missing data patterns across cohorts in order to maximize the statistical power in model training, as well as to accommodate missing information on the end-user side too, in order to maximize utility for the public.","PeriodicalId":73699,"journal":{"name":"Journal of data science : JDS","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71320795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A Hybrid Monitoring Procedure for Detecting Abnormality with Application to Energy Consumption Data 一种用于能耗数据异常检测的混合监测程序

Journal of data science : JDS Pub Date : 2022-01-01 DOI: 10.6339/22-jds1039

Daeyoung Lim, Ming-Hui Chen, N. Ravishanker, Mark Bolduc, Brian McKeon, Stanley Nolan

引用次数: 1