Multimodal machine learning in precision health: A sco** review

A Kline, H Wang, Y Li, S Dennis, M Hutch, Z Xu… - npj Digital …, 2022 - nature.com
Abstract Machine learning is frequently being leveraged to tackle problems in the health
sector including utilization for clinical decision-support. Its use has historically been focused …

Ensemble deep learning for Alzheimer's disease characterization and estimation

M Tanveer, T Goel, R Sharma, AK Malik… - Nature Mental …, 2024 - nature.com
Alzheimer's disease, which is characterized by a continual deterioration of cognitive abilities
in older people, is the most common form of dementia. Neuroimaging data, for example …

A review of deep transfer learning approaches for class-wise prediction of Alzheimer's disease using MRI images

PS Sisodia, GK Ameta, Y Kumar, N Chaplot - Archives of Computational …, 2023 - Springer
Alzheimer's disease is an irreversible, progressive neurodegenerative disorder that destroys
the brain and memory functionalities. In Alzheimer's disease, the brain starts shrinking, and …

[HTML][HTML] A hierarchical attention-based multimodal fusion framework for predicting the progression of Alzheimer's disease

P Lu, L Hu, A Mitelpunkt, S Bhatnagar, L Lu… - … Signal Processing and …, 2024 - Elsevier
Early detection and treatment can slow the progression of Alzheimer's Disease (AD), one of
the most common neurodegenerative diseases. Recent studies have demonstrated the …

Automatic analysis of MRI images for early prediction of Alzheimer's disease stages based on hybrid features of CNN and handcrafted features

A Khalid, EM Senan, K Al-Wagih, MM Ali Al-Azzam… - Diagnostics, 2023 - mdpi.com
Alzheimer's disease (AD) is considered one of the challenges facing health care in the
modern century; until now, there has been no effective treatment to cure it, but there are …

[HTML][HTML] Advances in multi-target fluorescent probes for imaging and analyzing biomarkers in Alzheimer's disease

C Zhu, J Han, F Liang, M Zhu, G Zhang… - Coordination Chemistry …, 2024 - Elsevier
Alzheimer's disease (AD) is a neurodegenerative condition that poses multiple challenges
for disease diagnosis and treatment. Biomarker-based assays are one of the important tools …

Regulation of neuroinflammation in Alzheimer's disease via nanoparticle-loaded phytocompounds with anti-inflammatory and autophagy-inducing properties

V Nayak, S Patra, S Rout, AB Jena, R Sharma… - Phytomedicine, 2024 - Elsevier
Background Alzheimer's disease (AD) is characterized by neuroinflammation linked to
amyloid β (Aβ) aggregation and phosphorylated tau (τ) protein in neurofibrillary tangles …

Radiomics, deep learning and early diagnosis in oncology

P Wei - Emerging topics in life sciences, 2021 - portlandpress.com
Medical imaging, including X-ray, computed tomography (CT), and magnetic resonance
imaging (MRI), plays a critical role in early detection, diagnosis, and treatment response …

Deep Learning Approaches for Early Prediction of Conversion from MCI to AD using MRI and Clinical Data: A Systematic Review

G Valizadeh, R Elahi, Z Hasankhani, HS Rad… - … Methods in Engineering, 2024 - Springer
Due to the absence of definitive treatment for Alzheimer's disease (AD), slowing its
development is essential. Accurately predicting the conversion of mild cognitive impairment …

[HTML][HTML] Ranking Influential Non-Content Factors on Scientific Papers' Citation Impact: A Multidomain Comparative Analysis

J Zhu, J Zhou, J Pan, F Gu, J Guo - Big Data and Cognitive Computing, 2025 - mdpi.com
The influence of scientific papers is measured by their citations. Although predicting the
papers' citation impact based on non-content factors has garnered extensive attention, the …