Results 71 to 80 of about 24,713,244 (334)

An Empirical Comparison of Multiple Imputation Methods for Categorical Data

, 2016
Multiple imputation is a common approach for dealing with missing values in statistical databases. The imputer fills in missing values with draws from predictive models estimated from the observed data, resulting in multiple, completed versions of the ...
Akande, Olanrewaju, Li, Fan, Reiter, Jerome   +2 more
core   +2 more sources

Remote Monitoring in Myasthenia Gravis: Exploring Symptom Variability

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Myasthenia gravis (MG) is a rare, autoimmune disorder characterized by fluctuating muscle weakness and potential life‐threatening crises. While continuous specialized care is essential, access barriers often delay timely interventions. To address this, we developed MyaLink, a telemedical platform for MG patients.
Maike Stein, Maximilian Mönch, Meret Herdick, Frauke Stascheit, Sarah Hoffmann, Hannah Preßler, Philipp Mergenthaler, Carla Dusemund, Paolo Doksani, Haoqi Sun, Pushpa Narayanaswami, Andreas Meisel, Lea Gerischer, Sophie Lehnerer   +13 more
wiley   +1 more source

Imputation Using Training Labels and Classification via Label Imputation

IEEE Access
Missing data is a common problem in practice, and various imputation methods have been developed to deal with missing data. However, even though in many cases, the labels are available in the training data, the common practice of imputation usually only ...
Thu Nguyen, Uyen Dang, Tuan L. Vo, Pal Halvorsen, Michael A. Riegler   +4 more
doaj   +1 more source

Comparison of Genotype Imputation for SNP Array and Low-Coverage Whole-Genome Sequencing Data

Frontiers in Genetics, 2022
Genotype imputation is the term used to describe the process of inferring unobserved genotypes in a sample of individuals. It is a key step prior to a genome-wide association study (GWAS) or genomic prediction.
Tianyu Deng, Pengfei Zhang, Dorian Garrick, Huijiang Gao, Lixian Wang, Fuping Zhao   +5 more
doaj   +1 more source

TREB: a BERT attempt for imputing tabular data imputation

CoRR
12 pages, 7 ...
Shuyue Wang, Wenjun Zhou, Han Jiang, Shuo Wang, Ren Zheng   +4 more
openaire   +2 more sources

Systematic Review on Missing Data Imputation Techniques with Machine Learning Algorithms for Healthcare

Journal of Robotics and Control (JRC), 2022
Missing data is one of the most common issues encountered in data cleaning process especially when dealing with medical dataset. A real collected dataset is prone to be incomplete, inconsistent, noisy and redundant due to potential reasons such as human ...
Amelia Ritahani Ismail, Nadzurah Zainal Abidin, M. Maen   +2 more
semanticscholar   +1 more source

Patterns of Postictal Abnormalities in Relation to Status Epilepticus in Adults

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Abnormalities on peri‐ictal diffusion‐weighted magnetic resonance imaging (DWI‐PMAs) are well‐established for patients with status epilepticus (SE), but knowledge on patterns of DWI‐PMAs and their prognostic impact is sparse. Methods This systematic review and individual participant data meta‐analysis included observational studies ...
Andrea Enerstad Bolle, Silvana Sarria‐Estrada, Thomas Bonduelle, Bertram Daniel Dynesen, Manuel Toledo, Zhu Chung Che, Kjell Heuser, Anastasios Chatzikonstantinou, Jérôme Aupy, Udaya Seneviratne, Estevo Santamarina Perez, Christoph Patrick Beier   +11 more
wiley   +1 more source

Practical strategies for handling breakdown of multiple imputation procedures

Emerging Themes in Epidemiology, 2021
Multiple imputation is a recommended method for handling incomplete data problems. One of the barriers to its successful use is the breakdown of the multiple imputation procedure, often due to numerical problems with the algorithms used within the ...
Cattram D. Nguyen, John B. Carlin, Katherine J. Lee   +2 more
doaj   +1 more source

Fractional Imputation in Survey Sampling: A Comparative Review [PDF]

, 2015
Fractional imputation (FI) is a relatively new method of imputation for handling item nonresponse in survey sampling. In FI, several imputed values with their fractional weights are created for each missing item.
Kim, Jae Kwang, Kim, Jae Kwang, Yang, Shu   +2 more
core   +4 more sources

Shared Genetic Effects and Antagonistic Pleiotropy Between Multiple Sclerosis and Common Cancers

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Epidemiologic studies have reported inconsistent altered cancer risk in individuals with multiple sclerosis (MS). Factors such as immune dysregulation, comorbidities, and disease‐modifying therapies may contribute to this variability.
Asli Buyukkurt, Marius Ygonia, Ali Manouchehrinia, Philippe Lefrançois, Ingrid Kockum, Adil Harroud   +5 more
wiley   +1 more source