Bronchoscopic Management of Prolonged Air Leaks With Endobronchial Valves in a Veteran Population
Sign Up to like & getrecommendations! Published in 2017 at "JAMA Surgery"
DOI: 10.1001/jamasurg.2016.3195
Abstract: the proportion of missing data for 5 of the 11 comorbidity variables included within the mFI increased over time. Specifically, the variables “history of myocardial infarction,” “history of percutaneous intervention, coronary stenting or cardiac surgery,”… read more here.
Keywords: management prolonged; bronchoscopic management; prolonged air; history ... See more keywords
Two‐stage‐neighborhood‐based multilabel classification for incomplete data with missing labels
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Intelligent Systems"
DOI: 10.1002/int.22861
Abstract: In recent years, it has been difficult for multilabel classification to obtain complete multilabel data in real‐world applications, and even a large number of labels for training samples are randomly missed. As a result, the… read more here.
Keywords: classification; multilabel classification; two stage; missing labels ... See more keywords
Hollow-tree: a metric access method for data with missing values
Sign Up to like & getrecommendations! Published in 2019 at "Journal of Intelligent Information Systems"
DOI: 10.1007/s10844-019-00567-8
Abstract: Similarity search is fundamental to store and retrieve large volumes of complex data required by many real world applications. A useful mechanism for such concept is the query-by-similarity. Based on their topological properties, metric similarity… read more here.
Keywords: missing values; metric access; access method; hollow tree ... See more keywords
Dimension-reduced empirical likelihood inference for response mean with data missing at random
Sign Up to like & getrecommendations! Published in 2017 at "Journal of Nonparametric Statistics"
DOI: 10.1080/10485252.2017.1339307
Abstract: ABSTRACT To make efficient inference for mean of a response variable when the data are missing at random and the dimension of covariate is not low, we construct three bias-corrected empirical likelihood (EL) methods in… read more here.
Keywords: dimension; response; dimension reduced; empirical likelihood ... See more keywords
Repositories for Taxonomic Data: Where We Are and What is Missing
Sign Up to like & getrecommendations! Published in 2020 at "Systematic Biology"
DOI: 10.1093/sysbio/syaa026
Abstract: Abstract Natural history collections are leading successful large-scale projects of specimen digitization (images, metadata, DNA barcodes), thereby transforming taxonomy into a big data science. Yet, little effort has been directed towards safeguarding and subsequently mobilizing… read more here.
Keywords: taxonomic studies; use; usepackage; repositories taxonomic ... See more keywords
Implementation of Instrumental Variable Bounds for Data Missing Not at Random
Sign Up to like & getrecommendations! Published in 2018 at "Epidemiology"
DOI: 10.1097/ede.0000000000000811
Abstract: Instrumental variables are routinely used to recover a consistent estimator of an exposure causal effect in the presence of unmeasured confounding. Instrumental variable approaches to account for nonignorable missing data also exist but are less… read more here.
Keywords: instrumental variable; instrumental variables; variable bounds; bounds data ... See more keywords
Knowledge Acquisition Approach Based on Incremental Objects From Data With Missing Values
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2913312
Abstract: Knowledge acquisition is the process of extracting useful knowledge from data sets to analyze data in areas of data mining and knowledge discovery. Most current knowledge acquisition work mainly focuses on static data. However, due… read more here.
Keywords: missing values; incremental objects; data missing; knowledge acquisition ... See more keywords
Logging Data Completion Based on an MC-GAN-BiLSTM Model
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2021.3138194
Abstract: Due to environmental interference and operational errors, problems such as incomplete and random missing logging data have occurred during the geophysical logging data collection process. Since it is difficult to establish a geophysical model based… read more here.
Keywords: network; bilstm model; gan bilstm; logging data ... See more keywords
Regularized approach for data missing not at random
Sign Up to like & getrecommendations! Published in 2019 at "Statistical Methods in Medical Research"
DOI: 10.1177/0962280217717760
Abstract: It is common in longitudinal studies that missing data occur due to subjects’ no response, missed visits, dropout, death or other reasons during the course of study. To perform valid analysis in this setting, data… read more here.
Keywords: approach data; regularized approach; missing random; data missing ... See more keywords
Why Are Data Missing in Clinical Data Warehouses? A Simulation Study of How Data Are Processed (and Can Be Lost).
Sign Up to like & getrecommendations! Published in 2023 at "Studies in health technology and informatics"
DOI: 10.3233/shti230103
Abstract: In recent years, the development of clinical data warehouses (CDW) has put Electronic Health Records (EHR) data in the spotlight. More and more innovative technologies for healthcare are based on these EHR data. However, quality… read more here.
Keywords: ehr data; data warehouses; simulation; data missing ... See more keywords
Image Recovery with Data Missing in the Presence of Salt-and-Pepper Noise
Sign Up to like & getrecommendations! Published in 2019 at "Applied Sciences"
DOI: 10.3390/app9071426
Abstract: In this paper, an image recovery problem under the case of salt-and-pepper noise and data missing that degrade image quality is addressed if they are not effectively handled, where the salt-and-pepper noise as the impulsive… read more here.
Keywords: pepper noise; salt pepper; data missing; image ... See more keywords