[HTML][HTML] One size does not fit all: methodological considerations for brain-based predictive modeling in psychiatry
Psychiatric illnesses are heterogeneous in nature. No illness manifests in the same way
across individuals, and no two patients with a shared diagnosis exhibit identical symptom …
across individuals, and no two patients with a shared diagnosis exhibit identical symptom …
Deep learning in large and multi-site structural brain MR imaging datasets
Large, multi-site, heterogeneous brain imaging datasets are increasingly required for the
training, validation, and testing of advanced deep learning (DL)-based automated tools …
training, validation, and testing of advanced deep learning (DL)-based automated tools …
[HTML][HTML] Accurate brain age prediction with lightweight deep neural networks
Deep learning has huge potential for accurate disease prediction with neuroimaging data,
but the prediction performance is often limited by training-dataset size and computing …
but the prediction performance is often limited by training-dataset size and computing …
Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning
Recent critical commentaries unfavorably compare deep learning (DL) with standard
machine learning (SML) approaches for brain imaging data analysis. However, their …
machine learning (SML) approaches for brain imaging data analysis. However, their …
Mind the gap: Performance metric evaluation in brain‐age prediction
Estimating age based on neuroimaging‐derived data has become a popular approach to
developing markers for brain integrity and health. While a variety of machine‐learning …
developing markers for brain integrity and health. While a variety of machine‐learning …
A downscaling approach for constructing high-resolution precipitation dataset over the Tibetan Plateau from ERA5 reanalysis
Current gridded precipitation datasets are hard to meet the requirements of hydrological and
meteorological applications in complex-terrain areas due to their coarse spatial resolution …
meteorological applications in complex-terrain areas due to their coarse spatial resolution …
Not just “big” data: Importance of sample size, measurement error, and uninformative predictors for developing prognostic models for digital interventions
ME McNamara, M Zisser, CG Beevers… - Behaviour research and …, 2022 - Elsevier
There is strong interest in developing a more efficient mental health care system. Digital
interventions and predictive models of treatment prognosis will likely play an important role …
interventions and predictive models of treatment prognosis will likely play an important role …
Machine-learning for the prediction of one-year seizure recurrence based on routine electroencephalography
É Lemoine, D Toffa, G Pelletier-Mc Duff, AQ Xu… - Scientific Reports, 2023 - nature.com
Predicting seizure recurrence risk is critical to the diagnosis and management of epilepsy.
Routine electroencephalography (EEG) is a cornerstone of the estimation of seizure …
Routine electroencephalography (EEG) is a cornerstone of the estimation of seizure …
Machine learning algorithm selection for windage alteration fault diagnosis of mine ventilation system
L Liu, J Liu, Q Zhou, D Huang - Advanced Engineering Informatics, 2022 - Elsevier
Abstract Machine learning algorithms have been widely used in mine fault diagnosis. The
correct selection of the suitable algorithms is the key factor that affects the fault diagnosis …
correct selection of the suitable algorithms is the key factor that affects the fault diagnosis …
[HTML][HTML] A comparison of resting state EEG and structural MRI for classifying Alzheimer's disease and mild cognitive impairment
Alzheimer's disease (AD) is the leading cause of dementia, accounting for 70% of cases
worldwide. By 2050, dementia prevalence will have tripled, with most new cases occurring …
worldwide. By 2050, dementia prevalence will have tripled, with most new cases occurring …