Results 61 to 70 of about 394,431 (266)

Lessons Learned From a Delayed‐Start Trial of Modafinil for Freezing of Gait in Parkinson's Disease

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Freezing of gait (FOG) in people with Parkinson's disease (PwPD) is debilitating and has limited treatments. Modafinil modulates beta/gamma band activity in the pedunculopontine nucleus (PPN), like PPN deep brain stimulation. We therefore tested the hypothesis that Modafinil would improve FOG in PwPD.
Tuhin Virmani, Lakshmi Pillai, Reid D. Landes, Aliyah Glover, Shannon Doerhoff, Jennifer Kleiner, Mitesh Lotia, Rohit Dhall, Edgar Garcia‐Rill   +8 more
wiley   +1 more source

BOBM: an adaptive deep learning framework for extended-window sepsis prediction with cross-institutional generalizability

Frontiers in Medicine
IntroductionSepsis, a life-threatening and costly condition, necessitates early detection for effective management. However, diagnostic and therapeutic delays are common.
Wanxuan Li, Wanxuan Li, Kai Xiao, Boyuan Gu, Boyuan Gu, Haoyu Zheng, Haoyu Zheng, Dan Shao, Ren Mo, Ren Mo, Qiang Wu, Qiang Wu, Qiong Yang   +12 more
doaj   +1 more source

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

Machine Learning Algorithms

, 2021
The first chapter of this book explained what machine learning is and why it is needed. This chapter now gives an in-depth overview of the subject. The most important machine learning algorithms (models) are explained in detail and several important questions are answered: Which algorithm should we select for the task?
Rajesh Singh, Anita Gehlot, Mahesh Kumar Prajapat, Bhupendra Singh   +3 more
openaire   +2 more sources

Learning Algorithms for Active Learning

, 2017
Accepted for publication at ICML ...
Bachman, Philip, Sordoni, Alessandro, Trischler, Adam   +2 more
openaire   +2 more sources

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

Dynamic systems control and identification using VSC-based learning algorithms for perceptron networks

Revista Técnica de la Facultad de Ingeniería, 2010
In this paper a set of Variable Structure Control (VSC)-based on-line learning algorithms for continuous time two layer and three layer perceptron networks with non-linear and linear activation functions are presented.
Francklin Rivas Echeverría, Eliezer Colina Morles   +1 more
doaj  

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

Causal Discovery in Astrophysics: Unraveling Supermassive Black Hole and Galaxy Coevolution

The Astrophysical Journal
Correlation does not imply causation, but patterns of statistical association between variables can be exploited to infer a causal structure (even with purely observational data) with the burgeoning field of causal discovery.
Zehao Jin, Mario Pasquato, Benjamin L. Davis, Tristan Deleu, Yu Luo, Changhyun Cho, Pablo Lemos, Laurence Perreault-Levasseur, Yoshua Bengio, Xi Kang, Andrea Valerio Macciò, Yashar Hezaveh   +11 more
doaj   +1 more source

Development of a Prediction Model for Progression Risk in High‐Grade Gliomas Based on Habitat Radiomics and Pathomics

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To investigate the value of constructing models based on habitat radiomics and pathomics for predicting the risk of progression in high‐grade gliomas. Methods This study conducted a retrospective analysis of preoperative magnetic resonance (MR) images and pathological sections from 72 patients diagnosed with high‐grade gliomas (52 ...
Yuchen Zhu, Yuxi Gong, Weilin Xu, Xingjian Sun, Gefei Jiang, Lei Qiu, Kexin Shi, Mengxing Wu, Yinjiao Fei, Jinling Yuan, Jinyan Luo, Yurong Li, Yuandong Cao, Minhong Pan, Shu Zhou   +14 more
wiley   +1 more source