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Journal articles on the topic 'Partially missing datasets'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Bottigliengo, Daniele, Giulia Lorenzoni, Honoria Ocagli, Matteo Martinato, Paola Berchialla, and Dario Gregori. "Propensity Score Analysis with Partially Observed Baseline Covariates: A Practical Comparison of Methods for Handling Missing Data." International Journal of Environmental Research and Public Health 18, no. 13 (2021): 6694. http://dx.doi.org/10.3390/ijerph18136694.

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(1) Background: Propensity score methods gained popularity in non-interventional clinical studies. As it may often occur in observational datasets, some values in baseline covariates are missing for some patients. The present study aims to compare the performances of popular statistical methods to deal with missing data in propensity score analysis. (2) Methods: Methods that account for missing data during the estimation process and methods based on the imputation of missing values, such as multiple imputations, were considered. The methods were applied on the dataset of an ongoing prospective

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Lee, Jae-Hyun, Je-Hyeon Yun, Jung-Suk Han, In-Sung Luke Yeo, and Hyung-In Yoon. "Repeatability of Intraoral Scanners for Complete Arch Scan of Partially Edentulous Dentitions: An In Vitro Study." Journal of Clinical Medicine 8, no. 8 (2019): 1187. http://dx.doi.org/10.3390/jcm8081187.

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Research on whether the number or location of missing teeth affects the accuracy of intraoral scanners in partial edentulous patients is scarce. This study aimed to evaluate the precision of complete-arch scan data of various partial edentulous arches acquired by intraoral scanners. Five different maxillary models were scanned using Carestream CS3600 and Medit i500 scanners. The models employed here were control: Fully dentate; Case 1: Missing a right second premolar and a first molar; Case 2: Missing a right second premolar, a first molar, both left premolars, and a left first molar; Case 3:

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FU, ZHENG, and TAO JIANG. "COMPUTING THE BREAKPOINT DISTANCE BETWEEN PARTIALLY ORDERED GENOMES." Journal of Bioinformatics and Computational Biology 05, no. 05 (2007): 1087–101. http://dx.doi.org/10.1142/s0219720007003107.

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The total order of genes or markers on a chromosome is crucial for most comparative genomics studies. However, current gene mapping efforts might only suffice to provide a partial order of the genes on a chromosome. Several different genes or markers might be mapped at the same position due to the low resolution of gene mapping or missing data. Moreover, conflicting datasets might give rise to the ambiguity of gene order. In this paper, we consider the reversal distance and breakpoint distance problems for partially ordered genomes. We first prove that these problems are nondeterministic polyn

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Leyrat, Clémence, Shaun R. Seaman, Ian R. White, et al. "Propensity score analysis with partially observed covariates: How should multiple imputation be used?" Statistical Methods in Medical Research 28, no. 1 (2017): 3–19. http://dx.doi.org/10.1177/0962280217713032.

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Inverse probability of treatment weighting is a popular propensity score-based approach to estimate marginal treatment effects in observational studies at risk of confounding bias. A major issue when estimating the propensity score is the presence of partially observed covariates. Multiple imputation is a natural approach to handle missing data on covariates: covariates are imputed and a propensity score analysis is performed in each imputed dataset to estimate the treatment effect. The treatment effect estimates from each imputed dataset are then combined to obtain an overall estimate. We cal

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Bartlett, Christopher W., Brett G. Klamer, Steven Buyske, Stephen A. Petrill, and William C. Ray. "Forming Big Datasets through Latent Class Concatenation of Imperfectly Matched Databases Features." Genes 10, no. 9 (2019): 727. http://dx.doi.org/10.3390/genes10090727.

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Informatics researchers often need to combine data from many different sources to increase statistical power and study subtle or complicated effects. Perfect overlap of measurements across academic studies is rare since virtually every dataset is collected for a unique purpose and without coordination across parties not-at-hand (i.e., informatics researchers in the future). Thus, incomplete concordance of measurements across datasets poses a major challenge for researchers seeking to combine public databases. In any given field, some measurements are fairly standard, but every organization col

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Mollentze, Nardus, Louis H. Nel, Sunny Townsend, et al. "A Bayesian approach for inferring the dynamics of partially observed endemic infectious diseases from space-time-genetic data." Proceedings of the Royal Society B: Biological Sciences 281, no. 1782 (2014): 20133251. http://dx.doi.org/10.1098/rspb.2013.3251.

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We describe a statistical framework for reconstructing the sequence of transmission events between observed cases of an endemic infectious disease using genetic, temporal and spatial information. Previous approaches to reconstructing transmission trees have assumed all infections in the study area originated from a single introduction and that a large fraction of cases were observed. There are as yet no approaches appropriate for endemic situations in which a disease is already well established in a host population and in which there may be multiple origins of infection, or that can enumerate

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Bartlett, Jonathan W., James R. Carpenter, Kate Tilling, and Stijn Vansteelandt. "Improving upon the efficiency of complete case analysis when covariates are MNAR." Biostatistics 15, no. 4 (2014): 719–30. http://dx.doi.org/10.1093/biostatistics/kxu023.

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Abstract Missing values in covariates of regression models are a pervasive problem in empirical research. Popular approaches for analyzing partially observed datasets include complete case analysis (CCA), multiple imputation (MI), and inverse probability weighting (IPW). In the case of missing covariate values, these methods (as typically implemented) are valid under different missingness assumptions. In particular, CCA is valid under missing not at random (MNAR) mechanisms in which missingness in a covariate depends on the value of that covariate, but is conditionally independent of outcome.

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Shi, Jiarong, Wei Yang, Longquan Yong, and Xiuyun Zheng. "Low-Rank Representation for Incomplete Data." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/439417.

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Low-rank matrix recovery (LRMR) has been becoming an increasingly popular technique for analyzing data with missing entries, gross corruptions, and outliers. As a significant component of LRMR, the model of low-rank representation (LRR) seeks the lowest-rank representation among all samples and it is robust for recovering subspace structures. This paper attempts to solve the problem of LRR with partially observed entries. Firstly, we construct a nonconvex minimization by taking the low rankness, robustness, and incompletion into consideration. Then we employ the technique of augmented Lagrange

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Koohafkan, Michael Connor, and Stanford Gibson. "Geomorphic trajectory and landform analysis using graph theory: A panel data approach to quantitative geomorphology." Progress in Physical Geography: Earth and Environment 42, no. 6 (2018): 679–96. http://dx.doi.org/10.1177/0309133318783143.

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Comparing successive datasets of GIS polygons derived from remote-sensing data is a common approach to quantify morphological change. GIS-derived datasets capture instantaneous observations or “snapshots” of the state of a system at a given time but do not explicitly capture the temporal sequences needed to characterize system processes. Comparisons between these “temporally-naive” datasets can be used to infer properties and trends of the landscape as a whole, but tracking changes in the characteristics of individual landforms (e.g. sandbars, dunes, or other surface features of interest) acro

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Chen, Brian, Bo Wu, Alireza Zareian, Hanwang Zhang, and Shih-Fu Chang. "General Partial Label Learning via Dual Bipartite Graph Autoencoder." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 10502–9. http://dx.doi.org/10.1609/aaai.v34i07.6621.

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We formulate a practical yet challenging problem: General Partial Label Learning (GPLL). Compared to the traditional Partial Label Learning (PLL) problem, GPLL relaxes the supervision assumption from instance-level — a label set partially labels an instance — to group-level: 1) a label set partially labels a group of instances, where the within-group instance-label link annotations are missing, and 2) cross-group links are allowed — instances in a group may be partially linked to the label set from another group. Such ambiguous group-level supervision is more practical in real-world scenarios

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Poulson, Dan, Jeff Bacon, Matt Durham, Elena Guardincerri, C. L. Morris, and Holly R. Trellue. "Application of muon tomography to fuel cask monitoring." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 377, no. 2137 (2018): 20180052. http://dx.doi.org/10.1098/rsta.2018.0052.

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Long-term monitoring of spent fuel stored in dry cask storage is currently achieved through the use of seals and surveillance. Muon tomography can provide direct imaging that may be useful in cases where what is known as Continuity of Knowledge (CoK) has been lost using the former methods. Over the past several years, a team from Los Alamos National Laboratory has been studying the use of muon scattering and stopping to examine spent fuel in dry cask storage. Data taken on a partially loaded Westinghouse MC-10 fuel cask have demonstrated that muon scattering radiography can detect missing fuel

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Hopkins, Melanie J., and Katherine St John. "A new family of dissimilarity metrics for discrete character matrices that include inapplicable characters and its importance for disparity studies." Proceedings of the Royal Society B: Biological Sciences 285, no. 1892 (2018): 20181784. http://dx.doi.org/10.1098/rspb.2018.1784.

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The use of discrete character data for disparity analyses has become more popular, partially due to the recognition that character data describe variation at large taxonomic scales, as well as the increasing availability of both character matrices co-opted from phylogenetic analysis and software tools. As taxonomic scope increases, the need to describe variation leads to some characters that may describe traits not found across all the taxa. In such situations, it is common practice to treat inapplicable characters as missing data when calculating dissimilarity matrices for disparity studies.

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Awrangjeb, Mohammad, Syed Gilani, and Fasahat Siddiqui. "An Effective Data-Driven Method for 3-D Building Roof Reconstruction and Robust Change Detection." Remote Sensing 10, no. 10 (2018): 1512. http://dx.doi.org/10.3390/rs10101512.

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Three-dimensional (3-D) reconstruction of building roofs can be an essential prerequisite for 3-D building change detection, which is important for detection of informal buildings or extensions and for update of 3-D map database. However, automatic 3-D roof reconstruction from the remote sensing data is still in its development stage for a number of reasons. For instance, there are difficulties in determining the neighbourhood relationships among the planes on a complex building roof, locating the step edges from point cloud data often requires additional information or may impose constraints,

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Guizilini, Vitor, and Fabio Ramos. "Learning to reconstruct 3D structures for occupancy mapping from depth and color information." International Journal of Robotics Research 37, no. 13-14 (2018): 1595–609. http://dx.doi.org/10.1177/0278364918783061.

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Real-world scenarios contain many structural patterns that, if appropriately extracted and modeled, can be used to reduce problems associated with sensor failure and occlusions while improving planning methods in such tasks as navigation and grasping. This paper devises a novel unsupervised procedure that models 3D structures from unorganized pointclouds as occupancy maps. Our methodology enables the learning of unique and arbitrarily complex features using a variational Bayesian convolutional auto-encoder, which compresses local information into a latent low-dimensional representation and the

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Hegele, Patrick, Jonas von Kobylinski, Leonhard Hitzler, Christian Krempaszky, and Ewald Werner. "In-Situ XRD Study of Phase Transformation Kinetics in a Co-Cr-W-Alloy Manufactured by Laser Powder-Bed Fusion." Crystals 11, no. 2 (2021): 176. http://dx.doi.org/10.3390/cryst11020176.

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The additive manufacturing process of laser powder-bed fusion (L-PBF) is an increasingly popular approach for patient-specific production of dental frameworks made from Co-Cr alloys. Macroscopically, frameworks produced in this way exhibit high anisotropy especially in Young’s modulus, and are missing standardized requirements. Microscopically, pronounced texture and high residual stresses are characteristic. To reduce resulting detrimental effects, the as-built (AB) parts are heat treated. Dependent on the treatment temperature, effects like the transformation of the γ-phase matrix in the AB

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Tadono, T., H. Nagai, H. Ishida, et al. "GENERATION OF THE 30 M-MESH GLOBAL DIGITAL SURFACE MODEL BY ALOS PRISM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B4 (June 13, 2016): 157–62. http://dx.doi.org/10.5194/isprsarchives-xli-b4-157-2016.

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Topographical information is fundamental to many geo-spatial related information and applications on Earth. Remote sensing satellites have the advantage in such fields because they are capable of global observation and repeatedly. Several satellite-based digital elevation datasets were provided to examine global terrains with medium resolutions e.g. the Shuttle Radar Topography Mission (SRTM), the global digital elevation model by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER GDEM). A new global digital surface model (DSM) dataset using the archived data of the Panc

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Tadono, T., H. Nagai, H. Ishida, et al. "GENERATION OF THE 30 M-MESH GLOBAL DIGITAL SURFACE MODEL BY ALOS PRISM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B4 (June 13, 2016): 157–62. http://dx.doi.org/10.5194/isprs-archives-xli-b4-157-2016.

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Topographical information is fundamental to many geo-spatial related information and applications on Earth. Remote sensing satellites have the advantage in such fields because they are capable of global observation and repeatedly. Several satellite-based digital elevation datasets were provided to examine global terrains with medium resolutions e.g. the Shuttle Radar Topography Mission (SRTM), the global digital elevation model by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER GDEM). A new global digital surface model (DSM) dataset using the archived data of the Panc

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Kaushik, Kavita, and Deepak Kumar. "Forest Disturbance Mapping with Microwave Remote Sensing." Journal of Landscape Ecology 13, no. 2 (2020): 95–128. http://dx.doi.org/10.2478/jlecol-2020-0012.

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AbstractIt is pre-requisite to conserve and protect the forest cover, therefore mapping of the forest distribution and monitoring of their temporal changes are also necessary. In the field of forestry, radar datasets have a high potential due to its ability to derive/extract information from the surface, sub-surface and even from the depth. The current work tries to utilize the capability of C-band radar datasets provided by Sentinel 1A/B mission to derive the required information for sensing the disturbances in the forest areas. Application of SAR or microwave remote sensing for forest distur

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Borchmann, Sven, Horst Müller, and Andreas Engert. "Seasonal Differences in Incidence and Survival of Hodgkin Lymphoma Depend on Geographic Location of Diagnosis." Blood 128, no. 22 (2016): 2996. http://dx.doi.org/10.1182/blood.v128.22.2996.2996.

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Abstract It has been previously described that Hodgkin lymphoma (HL) exhibits a seasonal incidence and mortality pattern. However, these findings arise from small datasets and are partially conflicting. The aim of the present study is to provide the most comprehensive analysis of seasonal variation in incidence and mortality risk of HL with a focus on geographic differences in a very large, high-quality dataset. All HL cases diagnosed between 1973 and 2012 in the 18 Surveillance, Epidemiology and End Results (SEER) registries were eligible (n=50,179). We excluded all death certificate and auto

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Zeng, Chuiqing, and Caren E. Binding. "Consistent Multi-Mission Measures of Inland Water Algal Bloom Spatial Extent Using MERIS, MODIS and OLCI." Remote Sensing 13, no. 17 (2021): 3349. http://dx.doi.org/10.3390/rs13173349.

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Envisat’s MERIS and its successor Sentinel OLCI have proven invaluable for documenting algal bloom conditions in coastal and inland waters. Observations over turbid eutrophic waters, in particular, have benefited from the band at 708 nm, which captures the reflectance peak associated with intense algal blooms and is key to line-height algorithms such as the Maximum Chlorophyll Index (MCI). With the MERIS mission ending in early 2012 and OLCI launched in 2016, however, time-series studies relying on these two sensors have to contend with an observation gap spanning four years. Alternate sensors

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Jiao, Peng, Kai Xu, Shiguang Yue, Xiangyu Wei, and Lin Sun. "A Decentralized Partially Observable Markov Decision Model with Action Duration for Goal Recognition in Real Time Strategy Games." Discrete Dynamics in Nature and Society 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/4580206.

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Multiagent goal recognition is a tough yet important problem in many real time strategy games or simulation systems. Traditional modeling methods either are in great demand of detailed agents’ domain knowledge and training dataset for policy estimation or lack clear definition of action duration. To solve the above problems, we propose a novel Dec-POMDM-T model, combining the classic Dec-POMDP, an observation model for recognizer, joint goal with its termination indicator, and time duration variables for actions with action termination variables. In this paper, a model-free algorithm named coo

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Tkachenko, Roman, Ivan Izonin, Natalia Kryvinska, Ivanna Dronyuk, and Khrystyna Zub. "An Approach towards Increasing Prediction Accuracy for the Recovery of Missing IoT Data based on the GRNN-SGTM Ensemble." Sensors 20, no. 9 (2020): 2625. http://dx.doi.org/10.3390/s20092625.

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The purpose of this paper is to improve the accuracy of solving prediction tasks of the missing IoT data recovery. To achieve this, the authors have developed a new ensemble of neural network tools. It consists of two successive General Regression Neural Network (GRNN) networks and one neural-like structure of the Successive Geometric Transformation Model (SGTM). The principle of ensemble topology construction on two successively connected general regression neural networks, supplemented with an SGTM neural-like structure, is mathematically substantiated, which improves the accuracy of predict

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Silvestro, Paolo Cosmo, Raffaele Casa, Jan Hanuš, et al. "Synergistic Use of Multispectral Data and Crop Growth Modelling for Spatial and Temporal Evapotranspiration Estimations." Remote Sensing 13, no. 11 (2021): 2138. http://dx.doi.org/10.3390/rs13112138.

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The aim of this research is to explore the analysis of methods allowing a synergetic use of information exchange between Earth Observation (EO) data and growth models in order to provide high spatial and temporal resolution actual evapotranspiration predictions. An assimilation method based on the Ensemble Kalman Filter algorithm allows for combining Sentinel-2 data with a new version of Simple Algorithm For Yield (SAFY_swb) that considers the effect of the water balance on yield and estimates the daily trend of evapotranspiration (ET). Our study is relevant in the context of demonstrating the

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Gumber, Amanda, and Michael J. Foster. "A MODIS-Derived Value-Added Climatology of Maritime Cloud Liquid Water Path That Conserves Solar Reflectance." Journal of Applied Meteorology and Climatology 56, no. 6 (2017): 1767–81. http://dx.doi.org/10.1175/jamc-d-16-0241.1.

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AbstractA dataset is generated from a method to retrieve distributions of cloud liquid water path over partially cloudy scenes. The method was introduced in a 2011 paper by Foster and coauthors that described the theory and provided test cases. Here it has been applied to Moderate Resolution Imaging Spectroradiometer (MODIS) collection-5 and collection-6 cloud products, resulting in a value-added dataset that contains adjusted distributions of cloud liquid water path for more than 10 years for marine liquid cloud for both Aqua and Terra. This method adjusts horizontal distributions of cloud op

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Russi, Calice, Ruggieri, et al. "Gastric Normal Adjacent Mucosa Versus Healthy and Cancer Tissues: Distinctive Transcriptomic Profiles and Biological Features." Cancers 11, no. 9 (2019): 1248. http://dx.doi.org/10.3390/cancers11091248.

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Gastric cancer (GC) is a leading cause of cancer-related deaths in the world. Molecular heterogeneity is a major determinant for the clinical outcomes and an exhaustive tumor classification is currently missing. Histologically normal tissue adjacent to the tumor (NAT) is commonly used as a control in cancer studies, nevertheless a recently published paper described the unique characteristics of the NAT in several tumor types. Little is known about the global gene expression profile of gastric NAT (gNAT) which could be an effective tool for a more realistic definition of GC molecular signature.

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Berkofsky-Fessler, Windy, Melanie-Jane McConnell, Donna S. Neuberg, and Jonathan D. Licht. "Engineered Expression of JAK2V617F Drastically Changes the Response of Progenitor Cells to Erythropoietin and Partially Mimics the Polycythemia Expression Signature." Blood 108, no. 11 (2006): 3597. http://dx.doi.org/10.1182/blood.v108.11.3597.3597.

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Abstract Polycythemia vera (PV) is a myeloproliferative disorder, consisting of an abnormal increase of red blood cells. Over 90% of PV patients harbor the JAK2V617F mutation. To determine the distinct role that JAK2V617F plays in the pathogenesis of PV, we transduced human bone marrow CD34+ cells with either wild-type (WT) or V617F (MT) JAK2 constructs, incubated them in cytokine mixtures designed to promote full myeloid outgrowth (+EPO) or the same mixture lacking erythropoietin (EPO) (−EPO). After 5 days of growth, flow cytometry showed MT cells had a 3-fold increase in erythroid differenti

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Roksvåg, Thea, Ingelin Steinsland, and Kolbjørn Engeland. "Estimation of annual runoff by exploiting long-term spatial patterns and short records within a geostatistical framework." Hydrology and Earth System Sciences 24, no. 8 (2020): 4109–33. http://dx.doi.org/10.5194/hess-24-4109-2020.

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Abstract. In this article, we present a Bayesian geostatistical framework that is particularly suitable for interpolation of hydrological data when the available dataset is sparse and includes both long and short records of runoff. A key feature of the proposed framework is that several years of runoff are modelled simultaneously with two spatial fields: one that is common for all years under study that represents the runoff generation due to long-term (climatic) conditions and one that is year-specific. The climatic spatial field captures how short records of runoff from partially gauged catc

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Shao, Zhenfeng, Penghao Tang, Zhongyuan Wang, Nayyer Saleem, Sarath Yam, and Chatpong Sommai. "BRRNet: A Fully Convolutional Neural Network for Automatic Building Extraction From High-Resolution Remote Sensing Images." Remote Sensing 12, no. 6 (2020): 1050. http://dx.doi.org/10.3390/rs12061050.

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Building extraction from high-resolution remote sensing images is of great significance in urban planning, population statistics, and economic forecast. However, automatic building extraction from high-resolution remote sensing images remains challenging. On the one hand, the extraction results of buildings are partially missing and incomplete due to the variation of hue and texture within a building, especially when the building size is large. On the other hand, the building footprint extraction of buildings with complex shapes is often inaccurate. To this end, we propose a new deep learning

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Dura, Ana, Theo J. Mertzimekis, Evangelos Bakalis, et al. "CTD data profiling to assess the natural hazard of active submarine vent fields: the case of Santorini Island." Bulletin of the Geological Society of Greece 56, no. 1 (2020): 70. http://dx.doi.org/10.12681/bgsg.20942.

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Almost three quarters of known volcanic activity on Earth occurs in underwater locations. The presence of active hydrothermal vent fields in such environments is a potential natural hazard for the environment, the society, and the economy. Despite its importance for risk assessment and risk mitigation, monitoring of the activity is impeded by the remoteness and the extreme conditions of underwater volcanoes. The large difference of population present on Santorini between the winter and summer seasons, all within a partially enclosed system, make the Santorini-Kolumbo volcanic field, an ideal p

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Kastrisios, Christos, Brian Calder, Giuseppe Masetti, and Peter Holmberg. "Validation of the shoal-biased pattern of bathymetric information on nautical charts." Abstracts of the ICA 1 (July 15, 2019): 1–2. http://dx.doi.org/10.5194/ica-abs-1-163-2019.

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<p><strong>Abstract.</strong> The compilation of nautical charts comprises a number of tasks that are often monotonous, time consuming, and, as such, prone to human error. A long-term goal of the hydrographic community has thus been the automation of the process due to the unquestionable advantages of automation to the accuracy, reliability, and consistency of products for a reduced cost-to-productivity ratio. One of the tasks that has received the attention of automation efforts is the compilation of bathymetry on charts and more precisely the generation and generalization o

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Miao, Wang, Wei Li, Wenjie Hu, Ruoyu Wang, and Zhi Geng. "Invited Commentary: Estimation and Bounds Under Data Fusion." American Journal of Epidemiology, July 7, 2021. http://dx.doi.org/10.1093/aje/kwab194.

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Abstract Ogburn et al. (Am J Epidemiol. 2021;000(0):000-000) raise a cautionary tale for epidemiological data fusion: bias may occur if a variable completely missing in the primary dataset is imputed according to a regression model estimated from an auxiliary dataset. However, in some specific settings, solution may exist. Focusing on a linear outcome regression model with a missing covariate, we show that the bias can be eliminated if the underlying imputation model for the missing covariate is nonlinear in the common variables measured in both datasets. Otherwise, we describe two alternative

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Sainju, Arpan Man, Wenchong He, Zhe Jiang, Da Yan, and Haiquan Chen. "Flood Inundation Mapping with Limited Observations Based on Physics-Aware Topography Constraint." Frontiers in Big Data 4 (July 26, 2021). http://dx.doi.org/10.3389/fdata.2021.707951.

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Spatial classification with limited observations is important in geographical applications where only a subset of sensors are deployed at certain spots or partial responses are collected in field surveys. For example, in observation-based flood inundation mapping, there is a need to map the full flood extent on geographic terrains based on earth imagery that partially covers a region. Existing research mostly focuses on addressing incomplete or missing data through data cleaning and imputation or modeling missing values as hidden variables in the EM algorithm. These methods, however, assume th

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Golovenkin, Sergey E., Jonathan Bac, Alexander Chervov, et al. "Trajectories, bifurcations, and pseudo-time in large clinical datasets: applications to myocardial infarction and diabetes data." GigaScience 9, no. 11 (2020). http://dx.doi.org/10.1093/gigascience/giaa128.

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Abstract Background Large observational clinical datasets are becoming increasingly available for mining associations between various disease traits and administered therapy. These datasets can be considered as representations of the landscape of all possible disease conditions, in which a concrete disease state develops through stereotypical routes, characterized by “points of no return" and “final states" (such as lethal or recovery states). Extracting this information directly from the data remains challenging, especially in the case of synchronic (with a short-term follow-up) observations.

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Raman, Rameela, Wencong Chen, Michael O. Harhay, et al. "Dealing with missing delirium assessments in prospective clinical studies of the critically ill: a simulation study and reanalysis of two delirium studies." BMC Medical Research Methodology 21, no. 1 (2021). http://dx.doi.org/10.1186/s12874-021-01274-1.

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Abstract Background In longitudinal critical care studies, researchers may be interested in summarizing an exposure over time and evaluating its association with a long-term outcome. For example, the number of days a patient has delirium (i.e., brain dysfunction) during their critical care stay is associated with the presence and severity of long-term cognitive problems. In large pragmatic trials and multicenter observational studies, particularly when electronic medical record data is used, the information on daily exposure status may be available at some time points and not at others. Model-

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Ling, Albee, Maria Montez-Rath, Maya Mathur, Kris Kapphahn, and Manisha Desai. "How to Apply Multiple Imputation in Propensity Score Matching with Partially Observed Confounders: A Simulation Study and Practical Recommendations." Journal of Modern Applied Statistical Methods 19, no. 1 (2021). http://dx.doi.org/10.22237/jmasm/1608552120.

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Propensity score matching (PSM) has been widely used to mitigate confounding in observational studies, although complications arise when the covariates used to estimate the PS are only partially observed. Multiple imputation (MI) is a potential solution for handling missing covariates in the estimation of the PS. However, it is not clear how to best apply MI strategies in the context of PSM. We conducted a simulation study to compare the performances of popular non-MI missing data methods and various MI-based strategies under different missing data mechanisms. We found that commonly applied mi

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Virdee, Pradeep S., Alice Fuller, Michael Jacobs, Tim Holt, and Jacqueline Birks. "Assessing data quality from the Clinical Practice Research Datalink: a methodological approach applied to the full blood count blood test." Journal of Big Data 7, no. 1 (2020). http://dx.doi.org/10.1186/s40537-020-00375-w.

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Abstract A Full Blood Count (FBC) is a common blood test including 20 parameters, such as haemoglobin and platelets. FBCs from Electronic Health Record (EHR) databases provide a large sample of anonymised individual patient data and are increasingly used in research. We describe the quality of the FBC data in one EHR. The Test dataset from the Clinical Research Practice Datalink (CPRD) was accessed, which contains results of tests performed in primary care, such as FBC blood tests. Medical codes and entity codes, two coding systems used within CPRD to identify FBC records, were compared, with

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Tkachenko, Roman, Ivan Izonin, Ivanna Dronyuk, Mykola Logoyda, and Pavlo Tkachenko. "Recover Missing Sensor Data with GRNN-based Cascade Scheme." International Journal of Sensors, Wireless Communications and Control 10 (August 13, 2020). http://dx.doi.org/10.2174/2210327910999200813151904.

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Background: Today, using of systems on the base of Internet of Things (ІоТ) devices is very widespread in various applications. Intellectual analysis of the data collected by similar devices is an important task for efficient and successful functioning of such systems. In particular, the reliability of such kind of analysis has greatly influence on the ability to partially or fully automate certain processes or subsystems. However, imperfect devices of data collection, transportation errors, etc. cause data missing to appear. A number of limitations cause this problem, and in the work, they ma

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Valero, Francisco P. J., Alexander Marshak, and Patrick Minnis. "Lagrange Point Missions: The Key to next Generation Integrated Earth Observations. DSCOVR Innovation." Frontiers in Remote Sensing 2 (September 20, 2021). http://dx.doi.org/10.3389/frsen.2021.745938.

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A new perspective for studying Earth processes has been soundly demonstrated by the Deep Space Climate Observatory (DSCOVR) mission. For the past 6 years, the first Earth-observing satellite orbiting at the Lagrange 1 (L1) point, the DSCOVR satellite has been viewing the planet in a fundamentally different way compared to all other satellites. It is providing unique simultaneous observations of nearly the entire sunlit face of the Earth at a relatively high temporal resolution. This capability enables detailed coverage of evolving atmospheric and surface systems over meso- and large-scale doma

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Nozères, Claude, and Mary Kennedy. "Mission: Implausible — Revealing rogue marine species in records across biodiversity data platforms." Biodiversity Information Science and Standards 3 (June 18, 2019). http://dx.doi.org/10.3897/biss.3.36002.

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Online biodiversity platforms publish datasets with graphic tools to help with quality control of submitted records, but more could be done to make the data robust for ecological analyses. Attention has focused mostly on automating tools for obvious errors, including misspelled names and synonyms, dates, or coordinates. However, a manual review of species identifications and distributions may uncover improbable records, such as a species reported in an area far from its usual range, or a rare species found in an area that has many more records of a related species. Examples are shown by constr

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Benschop, Jackie, Shahista Nisa, and Simon E. F. Spencer. "Still ‘dairy farm fever’? A Bayesian model for leptospirosis notification data in New Zealand." Journal of The Royal Society Interface 18, no. 175 (2021). http://dx.doi.org/10.1098/rsif.2020.0964.

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Routinely collected public health surveillance data are often partially complete, yet remain a useful source by which to monitor incidence and track progress during disease intervention. In the 1970s, leptospirosis in New Zealand (NZ) was known as ‘dairy farm fever’ and the disease was frequently associated with serovars Hardjo and Pomona. To reduce infection, interventions such as vaccination of dairy cattle with these two serovars was implemented. These interventions have been associated with significant reduction in leptospirosis incidence, however, livestock-based occupations continue to p

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Tietz, Stephan, Andy Mealor, Fern Leather, and Ali Dent. "Donor-based imputation methods for admin data: How to replace the number of rooms question on the Census." International Journal of Population Data Science 4, no. 3 (2019). http://dx.doi.org/10.23889/ijpds.v4i3.1299.

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The Census White Paper recommends removing the number of rooms question and to utilise administrative data instead. Previous research demonstrated that Valuation Office Agency (VOA) data can be used for this purpose in principal. However, users of Census micro-level data expect a utility dataset without missingness. This research project explored whether VOA number of rooms variable is suitable to undergo edit and imputation (E&I) within the standard census framework (i.e. donor imputation). We examined how linked admin and survey data challenges assumptions underlying the E&I pr

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Alvera-Azcárate, Aida, Dimitry Van der Zande, Alexander Barth, Charles Troupin, Samuel Martin, and Jean-Marie Beckers. "Analysis of 23 Years of Daily Cloud-Free Chlorophyll and Suspended Particulate Matter in the Greater North Sea." Frontiers in Marine Science 8 (September 13, 2021). http://dx.doi.org/10.3389/fmars.2021.707632.

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Satellite-derived estimates of ocean color variables are available for several decades now and allow performing studies of the long-term changes occurred in an ecosystem. A daily, gap-free analysis of chlorophyll (CHL) and suspended particulate matter (SPM, indicative of light availability in the subsurface) at 1 km resolution over the Greater North Sea during the period 1998–2020 is presented. Interannual changes are described, with maximum average CHL values increasing during the period 1998–2008, a slightly decreasing trend in 2009–2017 and an stagnation in recent years. The typical spring

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Yan, Rui, Fa Zhang, Xiaosong Rao, et al. "Richer fusion network for breast cancer classification based on multimodal data." BMC Medical Informatics and Decision Making 21, S1 (2021). http://dx.doi.org/10.1186/s12911-020-01340-6.

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Abstract Background Deep learning algorithms significantly improve the accuracy of pathological image classification, but the accuracy of breast cancer classification using only single-mode pathological images still cannot meet the needs of clinical practice. Inspired by the real scenario of pathologists reading pathological images for diagnosis, we integrate pathological images and structured data extracted from clinical electronic medical record (EMR) to further improve the accuracy of breast cancer classification. Methods In this paper, we propose a new richer fusion network for the classif

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