2期

最新文章

• A BAYESIAN APPROACH TO GRAPPA PARALLEL FMRI IMAGE RECONSTRUCTION INCREASES SNR AND POWER OF TASK DETECTION AI翻译
  作者：Sakitis, Chase J.; Rowe, Daniel B.
  作者单位：Marquette University
  摘要：In fMRI, capturing brain activation during a task is dependent on how quickly k-space arrays are obtained. Acquiring full k-space arrays, which are reconstructed into images using the inverse Fourier transform (IFT), that make up volume images can take a considerable amount of scan time. Undersampling k-space reduces the acquisition time but results in aliased, or (GRAPPA) is a parallel imaging technique that yields full images from subsampled arrays of k-space. GRAPPA uses localized interpola...
• A GENERAL FRAMEWORK OF BRAIN REGION DETECTION AND GENETIC VARIANTS SELECTION IN IMAGING GENETICS AI翻译
  作者：Su, Siqiang; Li, Zhenghao; Feng, Long; Li, Ting
  作者单位：University of Hong Kong; Hong Kong Polytechnic University
  摘要：Imaging genetics is a growing field that employs structural or functional neuroimaging techniques to study individuals with genetic risk variants potentially linked to specific illnesses. This area presents considerable challenges to statisticians due to the heterogeneous information and different data forms it involves. In addition, both imaging and genetic data are typically high-dimensional, creating a big data squared problem, Moreover, brain imaging data contains extensive spatial informa...
• HOLDOUT SETS FOR SAFE PREDICTIVE MODEL UPDATING AI翻译
  作者：Haidar-Wehbe, Sami; Emerson, Samuel R.; Aslett, Louis J. M.; Liley, James
  作者单位：Durham University
  摘要：Predictive risk scores for adverse outcomes are increasingly crucial in guiding health interventions. Such scores may need to be periodically updated due to change in the distributions they model. However, directly updating risk scores used to guide intervention can lead to biased risk estimates. To address this, we propose updating using a holdout set, a subset of the population that does not receive interventions guided by the risk score. Balancing the holdout set size is essential to ensure...
• SYNTHESIZING DATA PRODUCTS, MATHEMATICAL MODELS, AND OBSERVATIONS FOR LAKE TEMPERATURE FORECASTING AI翻译
  作者：Holthuijzen, Maike f.; Gramacy, Robert b.; Carey, Cayelan c.; Higdon, David m.; Thomas, R. quinn
  作者单位：Virginia Polytechnic Institute & State University; Virginia Polytechnic Institute & State University; Virginia Polytechnic Institute & State University; Virginia Polytechnic Institute & State University; Virginia Polytechnic Institute & State University; Virginia Polytechnic Institute & State University
  摘要：We present a novel forecasting framework for lake water temperature, which is crucial for managing lake ecosystems and drinking water resources. The General Lake Model (GLM) has been previously used for this purpose, but, similar to many process-based simulation models, it requires a large number of inputs (many of which are stochastic), presents challenges for uncertainty quantification (UQ), and can exhibit model bias. To address these issues, we propose a Gaussian process (GP) surrogate-bas...
• MODELING STRUCTURE AND COUNTRY-SPECIFIC HETEROGENEITY IN MISCLASSIFICATION MATRICES OF VERBAL AUTOPSY-BASED CAUSE OF DEATH CLASSIFIERS AI翻译
  作者：Pramanik, Sandipan; Zeger, Scott; Blau, Dianna; Datta, Abhirup
  作者单位：Johns Hopkins University; Centers for Disease Control & Prevention - USA
  摘要：Verbal autopsy (VA) algorithms are routinely used to determine individual-level causes of death (COD) in many low-and-middle-income countries. The individual CODs are then aggregated to derive population-level cause-specific mortality fractions (CSMF), which are essential to informing public health policies. However, VA algorithms frequently misclassify COD and introduce bias in CSMF estimates. A recent method, VA-calibration, can correct for this bias using a VA misclassification rate matrix ...
• CENSORED C-LEARNING FOR DYNAMIC TREATMENT REGIME IN COLORECTAL CANCER STUDY AI翻译
  作者：Zhan, Zishu; Liu, Zhishuai; Lin, Cunjie; Yi, Danhui; Liu, Jian; Yang, Yufei
  作者单位：Southern Medical University - China; Duke University; Renmin University of China; Renmin University of China; Beijing University of Chinese Medicine
  摘要：Dynamic treatment regimes (DTRs) represent sequential decision rules for multiple intervention stages. Each rule maps patients' covariates to optional treatments. The optimal dynamic treatment regime is the one that maximizes the mean outcome of interest if followed by the overall population. Motivated by a clinical study on the treatment of advanced colorectal cancer with traditional Chinese medicine, we propose a censored C-learning (CC-learning) method to estimate the DTR with multiple trea...
• A REWEIGHTED RANDOM FOREST TO PREDICT HEALTH OUTCOMES USING HUMAN MICROBIOME DATA AI翻译
  作者：Wang, Tian; Li, Bing; Xu, Huang; Miao, Yuqi; Qian, Min; Wang, Shuang
  作者单位：Columbia University; Brown University
  摘要：Many statistical methods examining associations and predictions of microbiome on health outcomes are distance-based, where several distance metrics are calculated to capture different aspects of microbiome and the optimal one is selected for final association or prediction. Studies have suggested that diverse forms of taxa are linked to health outcomes; that is, both abundant taxa in close proximity and rare taxa far away on the phylogenetic tree could be associated with or predictive of the s...
• ESTIMATING HETEROGENEOUS CAUSAL EFFECTS OF HIGH-DIMENSIONAL TREATMENTS: APPLICATION TO CONJOINT ANALYSIS AI翻译
  作者：Goplerud, Max; Imai, Kosuke; Pashley, Nicole E.
  作者单位：University of Texas System; University of Texas Austin; Harvard University; Harvard University; Rutgers University System; Rutgers University New Brunswick
  摘要：Estimation of heterogeneous treatment effects is an active area of research. Most of the existing methods, however, focus on estimating the conditional average treatment effects of a single, binary treatment given a set of pretreatment covariates. In this paper we propose a method to estimate the heterogeneous causal effects of high-dimensional treatments, which poses unique challenges in terms of estimation and interpretation. The proposed approach finds maximally heterogeneous groups and use...
• LEARN THEN TEST: CALIBRATING PREDICTIVE ALGORITHMS TO ACHIEVE RISK CONTROL AI翻译
  作者：Angelopoulos, Anastasios N.; Bates, Stephen; Candes, Emmanuel J.; Jordan, Michael, I; Lei, Lihua
  作者单位：University of California System; University of California Berkeley; Massachusetts Institute of Technology (MIT); Stanford University; Stanford University
  摘要：We introduce a framework for calibrating machine learning models to satisfy finite-sample statistical guarantees. Our calibration algorithms work with any model and (unknown) data-generating distribution and do not require retraining. The algorithms address, among other examples, false discovery rate control in multilabel classification, intersection-over-union control in instance segmentation, and simultaneous control of the type-1 outlier error and confidence set coverage in classification o...
• TEMPORAL NETWORK INFLUENCE MODEL WITH APPLICATION TO THE COVID-19 POPULATION FLOW NETWORK AI翻译
  作者：Zhang, Dongxue; Feng, Long; Wu, Yujia; Lan, Wei; Zhou, Jing
  作者单位：Southwestern University of Finance & Economics - China; Southwestern University of Finance & Economics - China; Nankai University; Nankai University; Renmin University of China; Renmin University of China
  摘要：Ever since its outbreak, COVID-19 has been rapidly spreading around the world and has become a significant threat to public health. Past experience has shown that, because of the incubation period, the contemporaneous population flow does not affect the contemporaneous number of cases, but the time-lagged population flow can affect case numbers. Moreover, the population flow networks of different lags can exhibit varying influences on the transmission of COVID-19. However, most existing studie...