Results 81 to 90 of about 95,782 (269)

Predicting Epileptogenic Tubers in Patients With Tuberous Sclerosis Complex Using a Fusion Model Integrating Lesion Network Mapping and Machine Learning

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Accurate localization of epileptogenic tubers (ETs) in patients with tuberous sclerosis complex (TSC) is essential but challenging, as these tubers lack distinct pathological or genetic markers to differentiate them from other cortical tubers.
Tinghong Liu, Qi Wang, Suhui Kuang, Dezhi Cao, Ping Ding, Shaohui Zhang, Haihua Wei, Zhirong Wei, Jinshan Xu, Xinyu Huang, Bing Liu, Shuli Liang   +11 more
wiley   +1 more source

Structural inference for linear regression with autocorrelated errors [PDF]

Statistische Hefte, 1975
Methods of structural inference are applied to the linear regression model in which the errors follow an autoregressive process. A marginal likelihood function is derived for the autoregressive parameters while structural distributions are obtained for the regression parameters.
openaire   +1 more source

Prognostic Implications of Sleep Architecture for Patients Admitted to the Intensive Care Unit With Status Epilepticus

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Status epilepticus (SE) is associated with significant mortality. Sleep architecture may reflect normal brain function. Impaired sleep architecture is associated with poorer outcomes in numerous conditions. Here we investigate the association of sleep architecture in continuous EEG (cEEG) with survival in SE.
Ran R. Liu, Marcus C. Ng, Gavin P. Winston, Sabrina Schaly, Garima Shukla, Lysa Boissé Lomax   +5 more
wiley   +1 more source

Likelihood Based Finite Sample Inference for Singly Imputed Synthetic Data Under the Multivariate Normal and Multiple Linear Regression Models

The Journal of Privacy and Confidentiality, 2015
In this paper we develop likelihood-based finite sample inference based on singly imputed partially synthetic data, when the original data follow either a multivariate normal or a multiple linear regression model.
Martin Klein, Bimal Sinha
doaj   +1 more source

Mapping wind erosion hazard with regression-based machine learning algorithms

Scientific Reports, 2020
Land susceptibility to wind erosion hazard in Isfahan province, Iran, was mapped by testing 16 advanced regression-based machine learning methods: Robust linear regression (RLR), Cforest, Non-convex penalized quantile regression (NCPQR), Neural network ...
Hamid Gholami, Aliakbar Mohammadifar, Dieu Tien Bui, Adrian L. Collins   +3 more
doaj   +1 more source

Scalable inference in functional linear regression with streaming data

, 2023
Traditional static functional data analysis is facing new challenges due to streaming data, where data constantly flow in. A major challenge is that storing such an ever-increasing amount of data in memory is nearly impossible. In addition, existing inferential tools in online learning are mainly developed for finite-dimensional problems, while ...
Xie, Jinhan, Shi, Enze, Sang, Peijun, Shang, Zuofeng, Jiang, Bei, Kong, Linglong   +5 more
openaire   +2 more sources

Cognitive Status in People With Epilepsy in the Republic of Guinea: A Prospective, Case–Control Study

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective People with epilepsy (PWE) may experience cognitive deficits but fail to undergo formal evaluation. This study compares cognitive status between PWE and healthy controls in the West African Republic of Guinea. Methods A cross‐sectional, case–control study was conducted in sequential recruitment phases (July 2024–July 2025) at Ignace ...
Maya L. Mastick, Cheick O. Soumah, Malé Doré, Oumar Mara, Desiré Neldje, Fodé A. Cissé, Toure M. Lamine, Aminata Diallo, Seungwon Lee, Siddharth Satish, Alexander J. X. Chen, Alice Liu, Nomin Enkhtsetseg, Farrah J. Mateen, The Guinea Epilepsy Project   +14 more
wiley   +1 more source

Gaussian Process Panel Modeling—Machine Learning Inspired Analysis of Longitudinal Panel Data

Frontiers in Psychology, 2020
In this article, we extend the Bayesian nonparametric regression method Gaussian Process Regression to the analysis of longitudinal panel data. We call this new approach Gaussian Process Panel Modeling (GPPM).
Julian D. Karch, Julian D. Karch, Andreas M. Brandmaier, Andreas M. Brandmaier, Manuel C. Voelkle   +4 more
doaj   +1 more source

Super‐Refractory Status Epilepticus (SRSE) in a Patient With Compound Heterozygous OPA1 Variants: Case Report and Literature Review

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Super‐Refractory Status Epilepticus (SRSE) is a rare, life‐threatening neurological emergency with unclear etiology in many cases. Mitochondrial dysfunction, often due to disease‐causing genetic variants, is increasingly recognized as a cause, with each gene producing distinct pathophysiological mechanisms.
Pouria Mohammadi, Lara Basovic, Christopher Michael McGraw   +2 more
wiley   +1 more source

ALS With and Without Upper Motor Neuron Signs: A Comparative Study Supporting the Gold Coast Criteria

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective The Gold Coast criteria permit diagnosis of amyotrophic lateral sclerosis (ALS) even without upper motor neuron (UMN) signs. However, whether ALS patients with UMN signs (ALSwUMN) and those without (ALSwoUMN) share similar characteristics and prognoses remains unclear.
Hee‐Jae Jung, E‐nae Cheong, Jungmin So, Heung‐Won Kang, Yangsean Choi, Eun‐Jae Lee, Hyunjin Kim, Young‐Min Lim   +7 more
wiley   +1 more source