Results 11 to 20 of about 533,821 (259)

Unicorn, Hare, or Tortoise? Using Machine Learning to Predict Working Memory Training Performance

Journal of Cognition, 2023
People differ considerably in the extent to which they benefit from working memory (WM) training. Although there is increasing research focusing on individual differences associated with WM training outcomes, we still lack an understanding of which ...
Yi Feng, Anja Pahor, Aaron R. Seitz, Dennis L. Barbour, Susanne M. Jaeggi   +4 more
doaj   +1 more source

Individual Factors Associated With COVID-19 Infection: A Machine Learning Study

Frontiers in Public Health, 2022
The fast, exponential increase of COVID-19 infections and their catastrophic effects on patients' health have required the development of tools that support health systems in the quick and efficient diagnosis and prognosis of this disease.
Tania Ramírez-del Real, Tania Ramírez-del Real, Mireya Martínez-García, Manlio F. Márquez, Laura López-Trejo, Guadalupe Gutiérrez-Esparza, Guadalupe Gutiérrez-Esparza, Enrique Hernández-Lemus, Enrique Hernández-Lemus   +8 more
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The Effect of Bioclimatic Covariates on Ensemble Machine Learning Prediction of Total Soil Carbon in the Pannonian Biogeoregion

Agronomy, 2023
This study employed an ensemble machine learning approach to evaluate the effect of bioclimatic covariates on the prediction accuracy of soil total carbon (TC) in the Pannonian biogeoregion. The analysis involved two main segments: (1) evaluation of base
Dorijan Radočaj, Mladen Jurišić, Vjekoslav Tadić   +2 more
doaj   +1 more source

Rehabilitation Machine for Bariatric Individuals

Machines, 2020
Obesity is known to be growing worldwide. The World Health Organization (WHO) reports that obesity has tripled since 1975. In 2016, 39% of adults over 18 years old were overweight, and 13% were obese. Obesity is mostly preventable by adopting lifestyle improvements, enhancing diet quality, and doing physical exercise.
Andrea Botta, Paride Cavallone, Luca Carbonari, Carmen Visconte, Giuseppe Quaglia   +4 more
openaire   +2 more sources

Machine learning model for malaria risk prediction based on mutation location of large-scale genetic variation data

Journal of Big Data, 2022
In recent malaria research, the complexity of the disease has been explored using machine learning models via blood smear images, environmental, and even RNA-Seq data. However, a machine learning model based on genetic variation data is still required to
Kah Yee Tai, Jasbir Dhaliwal
doaj   +1 more source

Decomposition of individual-specific and individual-shared components from resting-state functional connectivity using a multi-task machine learning method

NeuroImage, 2021
Resting-state functional connectivity (RSFC) can be used for mapping large-scale human brain networks during rest. There is considerable interest in distinguishing the individual-shared and individual-specific components in RSFC for the better ...
Xuetong Wang, Qiongling Li, Yan Zhao, Yirong He, Baoqiang Ma, Zhenrong Fu, Shuyu Li   +6 more
doaj   +1 more source

Individualized PATE: Differentially Private Machine Learning with Individual Privacy Guarantees

Proceedings on Privacy Enhancing Technologies, 2023
Applying machine learning (ML) to sensitive domains requires privacy protection of the underlying training data through formal privacy frameworks, such as differential privacy (DP). Yet, usually, the privacy of the training data comes at the cost of the resulting ML models' utility. One reason for this is that DP uses one uniform privacy budget epsilon
Franziska Boenisch, Christopher Mühl, Roy Rinberg, Jannis Ihrig, Adam Dziedzic   +4 more
openaire   +2 more sources

Machine Learning for Predicting Individual Severity of Blepharospasm Using Diffusion Tensor Imaging

Frontiers in Neuroscience, 2021
Accumulating diffusion tensor imaging (DTI) evidence suggests that white matter abnormalities evaluated by local diffusion homogeneity (LDH) or fractional anisotropy (FA) occur in patients with blepharospasm (BSP), both of which are significantly ...
Gang Liu, Gang Liu, Yanan Gao, Yanan Gao, Ying Liu, Ying Liu, Yaomin Guo, Yaomin Guo, Zhicong Yan, Zhicong Yan, Zilin Ou, Zilin Ou, Linchang Zhong, Chuanmiao Xie, Jinsheng Zeng, Jinsheng Zeng, Weixi Zhang, Weixi Zhang, Kangqiang Peng, Qingwen Lv   +19 more
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Use of Machine Learning for Dosage Individualization of Vancomycin in Neonates

Clinical Pharmacokinetics, 2023
High variability in vancomycin exposure in neonates requires advanced individualized dosing regimens. Achieving steady-state trough concentration (C0) and steady-state area-under-curve (AUC0-24) targets is important to optimize treatment. The objective was to evaluate whether machine learning (ML) can be used to predict these treatment targets to ...
Tang, Bo-Hao, Zhang, Jin-Yuan, Allegaert, Karel, Hao, Guo-Xiang, Yao, Bu-Fan, Leroux, Stephanie, Thomson, Alison, Yu, Ze, Gao, Fei, Zheng, Yi, Zhou, Yue, Capparelli, Edmund, Biran, Valerie, Simon, Nicolas, Meibohm, Bernd, Lo, Yoke-Lin, Marques, Remedios, Peris, Jose-Esteban, Lutsar, Irja, Saito, Jumpei, Jacqz-Aigrain, Evelyne, van den Anker, John, Wu, Yue-E, Zhao, Wei, Wu, Yue-E.   +24 more
openaire   +5 more sources

Influence of Individual Differences in fMRI-Based Pain Prediction Models on Between-Individual Prediction Performance

Frontiers in Neuroscience, 2018
Decoding subjective pain perception from functional magnetic resonance imaging (fMRI) data using machine learning technique is gaining a growing interest. Despite the well-documented individual differences in pain experience and brain responses, it still
Qianqian Lin, Qianqian Lin, Linling Li, Linling Li, Jia Liu, Jia Liu, Weixiang Liu, Weixiang Liu, Gan Huang, Gan Huang, Zhiguo Zhang, Zhiguo Zhang   +11 more
doaj   +1 more source