Results 51 to 60 of about 6,412,013 (303)
ABSTRACT Background Survivors of pediatric brain tumors (PBTs) can experience long‐term social difficulties, impacting quality of life. Beyond medical and environmental factors, family psychosocial risk may play a role in social outcomes by shaping the caregiving environment and may provide intervention options.
Renske H. Houben +4 more
wiley +1 more source
Gaussian Processes for Missing Value Imputation
Missing values are common in many real-life datasets. However, most of the current machine learning methods can not handle missing values. This means that they should be imputed beforehand. Gaussian Processes (GPs) are non-parametric models with accurate uncertainty estimates that combined with sparse approximations and stochastic variational inference
Bahram Jafrasteh +3 more
openaire +4 more sources
In this work, we used the MICE (Multivariate Imputation by Chained Equations) technique to impute missing daily data from six meteorological variables (precipitation, temperature, relative humidity, atmospheric pressure, wind speed and insolation) from ...
Rafaela Lisboa Costa +7 more
doaj +1 more source
ABSTRACT Background Allogeneic hematopoietic stem cell transplantation (alloHSCT) is an essential therapy for several malignant and nonmalignant diseases, but relapse and graft loss remain the principal threats to its success. Routine monitoring of chimerism and minimal residual disease (MRD) enables early detection of imminent recurrence and guides ...
Carmen Junk +10 more
wiley +1 more source
A new effective method for estimating missing values in the sequence data prior to phylogenetic analysis [PDF]
In this article we address the problem of phylogenetic inference from nucleic acid data containing missing bases. We introduce a new effective approach, called “Probabilistic estimation of missing values” (PEMV), allowing one to estimate unknown ...
Lapointe, François-Joseph +2 more
core +1 more source
A Survey on Multivariate Time Series Imputation Using Adversarial Learning
Multivariate time series (MTS) are captured in a great variety of real-world applications. However, analysing and modeling the data for classification and forecasting purposes can become very challenging if values are missing in the data set.
Anna Richter +4 more
doaj +1 more source
Pediatric Idiopathic Multicentric Castleman Disease Is Often Severe But Responsive to Siltuximab
ABSTRACT Background Idiopathic multicentric Castleman disease (iMCD) is a potentially fatal immunologic disorder marked by widespread lymphadenopathy and inflammation. Siltuximab, an interleukin‐6 (IL‐6) inhibitor, is the only FDA‐approved treatment for adult patients with iMCD.
Bridget Austin +17 more
wiley +1 more source
Lookahead selective sampling for incomplete data
Missing values in data are common in real world applications. There are several methods that deal with this problem. In this paper we present lookahead selective sampling (LSS) algorithms for datasets with missing values.
Abdallah Loai, Shimshoni Ilan
doaj +1 more source
Improving Load Forecasting of Electric Vehicle Charging Stations Through Missing Data Imputation
As the penetration of electric vehicles (EVs) accelerates according to eco-friendly policies, the impact of electric vehicle charging demand on a power distribution network is becoming significant for reliable power system operation.
Byungsung Lee, Haesung Lee, Hyun Ahn
doaj +1 more source
ABSTRACT Background Latino children are projected to make up nearly one‐third of United States (US) children by 2060, and many of their caregivers speak Spanish. Prior survey research has documented communication difficulties for Spanish‐speaking caregivers of children with cancer, but contemporary qualitative data are limited.
Jenny Ruiz +6 more
wiley +1 more source

