[HTML][HTML] Multimodal machine learning in precision health: A scoping review
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 …
sector including utilization for clinical decision-support. Its use has historically been focused …
Using machine learning approaches for multi-omics data analysis: A review
With the development of modern high-throughput omic measurement platforms, it has
become essential for biomedical studies to undertake an integrative (combined) approach to …
become essential for biomedical studies to undertake an integrative (combined) approach to …
[HTML][HTML] Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review
S Grueso, R Viejo-Sobera - Alzheimer's research & therapy, 2021 - Springer
Background An increase in lifespan in our society is a double-edged sword that entails a
growing number of patients with neurocognitive disorders, Alzheimer's disease being the …
growing number of patients with neurocognitive disorders, Alzheimer's disease being the …
Machine learning techniques for diagnosis of alzheimer disease, mild cognitive disorder, and other types of dementia
Alzheimer's disease (AD) is one of the most common form of dementia which mostly affects
elderly people. AD identification in early stages is a difficult task in medical practice and …
elderly people. AD identification in early stages is a difficult task in medical practice and …
[HTML][HTML] A deep learning based convolutional neural network model with VGG16 feature extractor for the detection of Alzheimer Disease using MRI scans
Alzheimer's disease (AD) is one of the most prevalent types of dementia, which primarily
affects people over age 60. In clinical practice, it is a challenging task to identify AD in its …
affects people over age 60. In clinical practice, it is a challenging task to identify AD in its …
[HTML][HTML] Information fusion and artificial intelligence for smart healthcare: a bibliometric study
With the fast progress in information technologies and artificial intelligence (AI), smart
healthcare has gained considerable momentum. By using advanced technologies like AI …
healthcare has gained considerable momentum. By using advanced technologies like AI …
[HTML][HTML] The contribution of gut microbiota–brain axis in the development of brain disorders
J Maiuolo, M Gliozzi, V Musolino, C Carresi… - Frontiers in …, 2021 - frontiersin.org
Different bacterial families colonize most mucosal tissues in the human organism such as
the skin, mouth, vagina, respiratory, and gastrointestinal districts. In particular, the …
the skin, mouth, vagina, respiratory, and gastrointestinal districts. In particular, the …
Two-stage deep learning model for Alzheimer's disease detection and prediction of the mild cognitive impairment time
Alzheimer's disease (AD) is an irreversible neurodegenerative disease characterized by
thinking, behavioral and memory impairments. Early prediction of conversion from mild …
thinking, behavioral and memory impairments. Early prediction of conversion from mild …
[HTML][HTML] Review on alzheimer disease detection methods: Automatic pipelines and machine learning techniques
Alzheimer's Disease (AD) is becoming increasingly prevalent across the globe, and various
diagnostic and detection methods have been developed in recent years. Several techniques …
diagnostic and detection methods have been developed in recent years. Several techniques …
A systematic literature review on multimodal machine learning: Applications, challenges, gaps and future directions
Multimodal machine learning (MML) is a tempting multidisciplinary research area where
heterogeneous data from multiple modalities and machine learning (ML) are combined to …
heterogeneous data from multiple modalities and machine learning (ML) are combined to …