Results 61 to 70 of about 202,004 (267)
ABSTRACT Objective Down syndrome regression disorder is a syndrome characterized by subacute loss of cognitive, behavioral, and functional abilities in individuals with Down syndrome. Electroencephalography abnormalities are frequently observed during evaluation, but it remains unclear whether these findings represent a dynamic marker of disease ...
Jonathan D. Santoro +14 more
wiley +1 more source
In today's technological landscape, where data processing forms the backbone of modeling and predictive analytics, data imputation is crucial in filling missing values within datasets using statistical techniques.
Vartul Shrivastava, Shekhar Shukla
doaj +1 more source
Feature Based Multivariate Data Imputation [PDF]
We investigate a new multivariate data imputation approach for dealing with variety of types of missingness. The proposed approach relies on the aggregation of the most suitable methods from a multitude of imputation techniques, adjusted to each feature of the dataset.
Alessio Petrozziello, Ivan Jordanov
openaire +2 more sources
Digital Cognitive Phenotyping for Differential Diagnosis and Monitoring in Neurological Conditions
ABSTRACT Objective To assess the utility, accessibility, and equivalence to supervised scales of online cognitive assessment in older individuals with cognitive impairment. Methods Patients with Alzheimer's disease (AD, n = 31), idiopathic normal pressure hydrocephalus (iNPH, n = 26), and traumatic brain injury (TBI, n = 23) completed online cognitive ...
Martina Del Giovane +10 more
wiley +1 more source
PERFORMANCE COMPARISON OF MISSFOREST AND MICE IN HANDLING MISSING CATEGORICAL DATA
Missing data represent a common challenge in statistical modeling and can substantially reduce the performance of classification algorithms. This study examines the impact of missing values on the performance of the XGBoost model by considering ...
Nurhidayah Nurhidayah +2 more
doaj +1 more source
Outcome-sensitive multiple imputation: a simulation study
Background Multiple imputation is frequently used to deal with missing data in healthcare research. Although it is known that the outcome should be included in the imputation model when imputing missing covariate values, it is not known whether it should
Evangelos Kontopantelis +3 more
doaj +1 more source
ABSTRACT Background Factors associated with relapse course and disability in myelin oligodendrocyte glycoprotein antibody‐associated disease (MOGAD) remain incompletely understood. Objectives To identify clinical and modifiable factors associated with relapse and disability in MOGAD. Methods In this ambispective multicentre cohort study using data from
Yingtao Wang +23 more
wiley +1 more source
weIMPUTE: a user-friendly web-based genotype imputation platform
BackgroundGenotype imputation is a critical preprocessing step in genome-wide association studies (GWAS), enhancing statistical power for detecting associated single nucleotide polymorphisms (SNPs) by increasing marker size.ResultsIn response to the ...
Mingliang Li +15 more
doaj +1 more source
Multiple imputation for handling missing outcome data when estimating the relative risk
Background Multiple imputation is a popular approach to handling missing data in medical research, yet little is known about its applicability for estimating the relative risk.
Thomas R. Sullivan +3 more
doaj +1 more source
Long‐Term Neurologic Exam Findings in People Diagnosed and Treated During Acute HIV Infection
ABSTRACT Objective Evaluate clinical and laboratory correlates of abnormal neurologic exam findings after acute HIV infection (AHI). Methods Participants from the RV254/SEARCH 010 cohort in Bangkok underwent standardized neurologic examinations at Weeks 0 (AHI), 12, 96, and 288 following antiretroviral therapy (ART).
Kathryn B. Holroyd +118 more
wiley +1 more source

