2025

最新文章

• THOMPSON SAMPLING FOR ZERO-INFLATED COUNT OUTCOMES WITH AN APPLICATION TO THE DRINK LESS MOBILE HEALTH STUDY AI翻译
  作者：Liu, Xueqing; Deliu, Nina; Chakraborty, Tanujit; Bell, Lauren; Chakraborty, Bibhas
  作者单位：National University of Singapore; Sapienza University Rome; University of London; King's College London
  摘要：Mobile health (mHealth) interventions often aim to improve distal outcomes, such as clinical conditions, by optimizing proximal outcomes through just-in-time adaptive interventions. Contextual bandits provide a suitable framework for customizing such interventions according to individual time-varying contexts. However, unique challenges, such as modeling count outcomes within bandit frameworks, have hindered the widespread application of contextual bandits to mHealth studies. The current work ...
• ANALYZING ENVIRONMENTAL BIOASSAYS WITH SPATIAL ODDS, RISK, AND SURVIVAL PROBABILITY RATIO REGRESSIONS AI翻译
  作者：Mondal, Debashis; Chang, Xiaohui
  作者单位：Washington University (WUSTL); Oregon State University
  摘要：Environmental bioassays, such as sediment toxicity tests, provide abroad survey of toxicity that is crucial for the conservation and protection of marine and estuarine ecosystems. Using odds, risk, and survival probability ratios, this paper presents a critical evaluation of sediment toxicity tests data collected in the New York-New Jersey harbor area. It further derives spatial regression analysis to combine test results, predict toxicity at unsampled locations, and determine the effects of s...
• BAYESIAN DATA AUGMENTATION FOR RECURRENT EVENTS UNDER INTERMITTENT ASSESSMENT IN OVERLAPPING INTERVALS WITH APPLICATIONS TO EMR DATA AI翻译
  作者：Liu, Xin; Schnell, Patrick M.
  作者单位：University System of Ohio; Ohio State University
  摘要：Electronic medical records (EMR) data contain rich information that can facilitate health-related studies but is collected primarily for purposes other than research. For recurrent events, EMR data often do not record event times or counts but only contain intermittently assessed and censored observations (i.e., upper and/or lower bounds for counts in a time interval) at uncontrolled times. This can result in noncontiguous or overlapping assessment intervals with censored event counts. Existin...
• INFERRING MECHANISTIC PARAMETERS OF SOMATIC HYPERMUTATION USING NEURAL NETWORKS AND APPROXIMATE BAYESIAN COMPUTATION AI翻译
  作者：Fisher, Thayer; Sung, Kevin; Simon, Noah; Fukuyama, Julia; Matsen, Frederick A.
  作者单位：University of Washington; University of Washington Seattle; Fred Hutchinson Cancer Center; Indiana University System; Indiana University Bloomington
  摘要：Somatic hypermutation (SHM) is a critical enzyme-mediated process of the adaptive immune response in which antibodies acquire mutations to enhance antigen binding. Despite abundant research elucidating the biochemical basis of SHM, and substantial sequence data available for parameterization, previous computational models of SHM have not been explicitly mechanistic. In this paper we bridge this gap by developing a probabilistic latent variable model encapsulating a sequence of interacting step...
• LEVERAGING CELLPHONE-DERIVED MOBILITY NETWORKS TO ASSESS COVID-19 TRAVEL RISK AI翻译
  作者：Slater, Justin J.; Brown, Patrick E.; Rosenthal, Jeffrey S.; Mateu, Jorge
  作者单位：University of Guelph; University of Toronto; Universitat Jaume I
  摘要：Since the beginning of the Covid-19 pandemic, public health authorities across the globe have implemented policies, such as lockdowns, in an attempt to reduce population mobility and, consequently, person-to-person contacts. It is well known that lockdowns reduce mobility, but to what extent does this reduction in mobility lead to lower infection rates? In this paper we extend the endemic-epidemic modeling framework in a principled manner, incorporating temporally changing mobility network dat...
• NONPARAMETRIC ADDITIVE VALUE FUNCTIONS: INTERPRETABLE REINFORCEMENT LEARNING WITH AN APPLICATION TO SURGICAL RECOVERY AI翻译
  作者：Medom-Nnamdi, Patrick; Smith, Timothy R.; Onnela, Jukka-Pekka; Lu, Junwei
  作者单位：Harvard University; Harvard University; Harvard University Medical Affiliates; Brigham & Women's Hospital; Harvard Medical School
  摘要：We propose a nonparametric additive model for estimating interpretable value functions in reinforcement learning, with an application in optimizing postoperative recovery through personalized, adaptive recommendations. While reinforcement learning has achieved significant success in various domains, recent methods often rely on black-box approaches, such as neural networks, which hinder the examination of individual feature contributions to a decision-making policy. Our novel method offers a f...
• ULTRA-SPARSE SMALL AREA ESTIMATION WITH SUPER HEAVY-TAILED PRIORS FOR INTERNAL MIGRATION FLOWS AI翻译
  作者：Fuquene-Patino, Jairo; Betancourt, Brenda
  作者单位：University of California System; University of California Davis; University of Chicago
  摘要：Migration flows represent an important component of global sustainable development and demographic trends. However, the dynamic nature of the migration phenomenon, known issues of undercoverage of administrative records and long intercensal periods make estimation of internal migration a very challenging task. In this work we focus on the estimation of internal migration in Colombia, which is the subject of an ongoing armed conflict that has triggered forced and voluntary population movements ...
• STATISTICAL INFERENCE FOR REGRESSION WITH IMPUTED BINARY COVARIATES WITH APPLICATION TO EMOTION RECOGNITION AI翻译
  作者：Lin, Ziqian; Huang, Danyang; Xiong, Ziyu; Wang, Hansheng
  作者单位：Peking University; Renmin University of China; Renmin University of China
  摘要：In the flourishing live streaming industry, accurate recognition of streamers' emotions has become a critical research focus with profound implications for audience engagement and content optimization. However, precise emotion coding typically requires manual annotation by trained experts, making it extremely expensive and time-consuming to obtain complete observational data for large-scale studies. Motivated by this challenge in streamer emotion recognition, we develop here a novel imputation...
• POD-BIN: A PROBABILITY OF DECISION BAYESIAN INTERVAL DESIGN FOR TIME-TO-EVENT DOSE-FINDING TRIALS WITH MULTIPLE TOXICITY GRADES AI翻译
  作者：Liu, Meizi; Lin, Ji; Mi, Gu; Lorenzato, Christelle; Chen, Xun; Ji, Yuan
  作者单位：Sanofi-Aventis; Sanofi USA; University of Chicago
  摘要：We introduce a Bayesian framework centered on the probability of decision for designing dose-finding trials. The proposed PoD-BIN design evaluates the posterior predictive probabilities of up-and-down decisions. In PoDBIN, multiple grades of toxicity, categorized as mild toxicity (MT) and doselimiting toxicity (DLT), are simultaneously modeled, with the primary outcome being time-to-toxicity for both MT and DLT. This approach allows the enrollment of new patients while previously enrolled pati...
• MORE POWER TO YOU: USING MACHINE LEARNING TO AUGMENT HUMAN CODING FOR MORE EFFICIENT INFERENCE IN TEXT-BASED RANDOMIZED TRIALS AI翻译
  作者：Mozer, Reagan; Miratrix, Luke
  作者单位：Bentley University; Harvard University
  摘要：For randomized trials that use text as an outcome, traditional approaches for assessing treatment impact require that each document first be manually coded for constructs of interest by trained human raters. This process, the current standard, is both time-consuming and limiting: even the largest human coding efforts are typically constrained to measure only a small set of dimensions across a subsample of available texts. In this work we present an inferential framework that can be used to inc...