Results 71 to 80 of about 1,043,746 (297)

Higher Amyloid and Tau Burden Is Associated With Faster Decline on a Digital Cognitive Test

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective A 2‐min digital clock‐drawing test (DCTclock) captures more granular features of the clock‐drawing process than the pencil‐and‐paper clock‐drawing test, revealing more subtle deficits at the preclinical stage of Alzheimer's disease (AD). A previous cross‐sectional study demonstrated that worse DCTclock performance was associated with
Jessie Fanglu Fu, Talia Robinson, Marina Rodriguez Alonso, Adela Francis Malave, Roos J. Jutten, Michael J. Properzi, Brian C. Healy, Jackson C. Thompson, Grace Del Carmen Montenegro, Emma Thibault, Dana Penney, Randall Davis, Reisa A. Sperling, Kathryn V. Papp, Keith A. Johnson, Julie C. Price, Dorene M. Rentz   +16 more
wiley   +1 more source

Portable Low‐Field Magnetic Resonance Imaging in People With Human Immunodeficiency Virus

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective The aging population of people with HIV (PWH) raises heightened concerns regarding accelerated aging and dementia. Portable, low‐field MRI (LF‐MRI) is an innovative technology that could enhance access and facilitate routine monitoring of PWH.
Annabel Sorby‐Adams, Malachi Keo, Jennifer Guo, Daire Daly, Richard Ahern, Kimon Zachary, Gregory Robbins, Rajesh T. Gandhi, Bragi Sveinsson, Adam de Havenon, Kevin N. Sheth, Otto Rapalino, Juan Eugenio Iglesias Gonzales, W. Taylor Kimberly, Shibani S. Mukerji   +14 more
wiley   +1 more source

Machine learning wavefunction

, 2023
To be published in the upcoming book "Quantum Chemistry in the Age of Machine Learning", edited by P ...
openaire   +2 more sources

A Probabilistic Adversarial Autoencoder for Novelty Detection: Leveraging Lightweight Design and Reconstruction Loss

IEEE Access
A novelty detection task involves identifying whether a data point is an outlier, given a training dataset that primarily captures the distribution of inliers. The novel class is usually absent, poorly sampled, or not well defined in the training data. A
Muhammad Asad, Ihsan Ullah, Muhammad Adeel Hafeez, Ganesh Sistu, Michael G. Madden   +4 more
doaj   +1 more source

3D scattering transforms for disease classification in neuroimaging

NeuroImage: Clinical, 2017
Classifying neurodegenerative brain diseases in MRI aims at correctly assigning discrete labels to MRI scans. Such labels usually refer to a diagnostic decision a learner infers based on what it has learned from a training sample of MRI scans ...
Tameem Adel, Taco Cohen, Matthan Caan, Max Welling   +3 more
doaj   +1 more source

Prediction and analysis of COVID-19 daily new cases and cumulative cases: times series forecasting and machine learning models [PDF]

, 2022
Yanding Wang, Zehui Yan, Ding Wang, Meitao Yang, Zhiqiang Li, Xinran Gong, Di Wu, Lingling Zhai, Wenyi Zhang, Yong Wang   +9 more
openalex   +1 more source

Prediction of Myasthenia Gravis Worsening: A Machine Learning Algorithm Using Wearables and Patient‐Reported Measures

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Myasthenia gravis (MG) is a rare disorder characterized by fluctuating muscle weakness with potential life‐threatening crises. Timely interventions may be delayed by limited access to care and fragmented documentation. Our objective was to develop predictive algorithms for MG deterioration using multimodal telemedicine data ...
Maike Stein, Haoqi Sun, Sophie Lehnerer, Lea Gerischer, Maximilian Mönch, Christian Meisel, Andreas Meisel, Pushpa Narayanaswami   +7 more
wiley   +1 more source

Qudit machine learning

Machine Learning: Science and Technology
Abstract We present a comprehensive investigation into the learning capabilities of a simple d-level system (qudit). Our study is specialized for classification tasks using real-world databases, specifically the Iris, breast cancer, and MNIST datasets.
Sebastián Roca-Jerat, Juan Román-Roche, David Zueco   +2 more
openaire   +6 more sources

Hospital Readmission After Traumatic Brain Injury Hospitalization in Community‐Dwelling Older Adults

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To examine the risk of hospital readmission after an index hospitalization for TBI in older adults. Methods Using data from the Atherosclerosis Risk in Communities (ARIC) study, we used propensity score matching of individuals with an index TBI‐related hospitalization to individuals with (1) non‐TBI hospitalizations (primary analysis)
Rachel Thomas, Connor A. Law, Joan A. Casey, Thomas Mosley, Rebecca F. Gottesman, Ramon Diaz‐Arrastia, Holly Elser, Andrea L. C. Schneider   +7 more
wiley   +1 more source

Climate-invariant machine learning [PDF]

Science Advances
Projecting climate change is a generalization problem: We extrapolate the recent past using physical models across past, present, and future climates. Current climate models require representations of processes that occur at scales smaller than model grid size, which have been the main source of model projection uncertainty. Recent machine learning (ML)
Tom Beucler, Pierre Gentine, Janni Yuval, Ankitesh Gupta, Liran Peng, Jerry Lin, Sungduk Yu, Stephan Rasp, Fiaz Ahmed, Paul A. O’Gorman, J. David Neelin, Nicholas J. Lutsko, Michael Pritchard   +12 more
openaire   +5 more sources