Results 71 to 80 of about 1,256,985 (257)

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

Deep Learning Approaches for Detecting of Nascent Geographic Atrophy in Age-Related Macular Degeneration

Ophthalmology Science
Purpose: Nascent geographic atrophy (nGA) refers to specific features seen on OCT B-scans, which are strongly associated with the future development of geographic atrophy (GA).
Heming Yao, PhD, Zhichao Wu, BAppSc(Optom), PhD, Simon S. Gao, PhD, Robyn H. Guymer, MBBS, PhD, Verena Steffen, MSc, Hao Chen, PhD, Mohsen Hejrati, PhD, Miao Zhang, PhD   +7 more
doaj   +1 more source

Label Distribution Learning

, 2016
Although multi-label learning can deal with many problems with label ambiguity, it does not fit some real applications well where the overall distribution of the importance of the labels matters.
Geng, Xin
core   +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

Multi-Instance Learning with Key Instance Shift [PDF]

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017
Multi-instance learning (MIL) deals with the tasks where each example is represented by a bag of instances. A bag is positive if it contains at least one positive instance, and negative otherwise. The positive instances are also called key instances. Only bag labels are observed, whereas specific instance labels are not available in MIL.
Ya-Lin Zhang, Zhi-Hua Zhou
openaire   +1 more source

Novelty Detection Under Multi-Instance Multi-Label Framework

, 2013
Novelty detection plays an important role in machine learning and signal processing. This paper studies novelty detection in a new setting where the data object is represented as a bag of instances and associated with multiple class labels, referred to ...
Briggs, Forrest, Fern, Xiaoli Z., Lou, Qi, Raich, Raviv   +3 more
core   +1 more source

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

Contrastive Mask Learning for Self-Supervised 3D Skeleton-Based Action Recognition

Sensors
In this paper, we propose a contrastive mask learning (CML) method for self-supervised 3D skeleton-based action recognition. Specifically, the mask modeling mechanism is integrated into multi-level contrastive learning with the aim of forming a mutually ...
Haoyuan Zhang
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

Multi Instance Learning For Unbalanced Data

, 2018
In the context of Multi Instance Learning, we analyze the Single Instance (SI) learning objective. We show that when the data is unbalanced and the family of classifiers is sufficiently rich, the SI method is a useful learning algorithm. In particular, we show that larger data imbalance, a quality that is typically perceived as negative, in fact ...
Kozdoba, Mark, Moroshko, Edward, Shani, Lior, Takagi, Takuya, Katoh, Takashi, Mannor, Shie, Crammer, Koby   +6 more
openaire   +2 more sources