Multimodal machine learning in precision health: A sco** 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 …
Healthcare Internet of Things (H-IoT): Current trends, future prospects, applications, challenges, and security issues
Advancements in Healthcare Internet of Things (H-IoT) systems have created new
opportunities and solutions for healthcare services, including the remote treatment and …
opportunities and solutions for healthcare services, including the remote treatment and …
A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer's disease
Alzheimer's disease (AD) is the most common type of dementia. Its diagnosis and
progression detection have been intensively studied. Nevertheless, research studies often …
progression detection have been intensively studied. Nevertheless, research studies often …
An overview of deep learning methods for multimodal medical data mining
Deep learning methods have achieved significant results in various fields. Due to the
success of these methods, many researchers have used deep learning algorithms in …
success of these methods, many researchers have used deep learning algorithms in …
Machine learning and deep learning approaches for brain disease diagnosis: principles and recent advances
Brain is the controlling center of our body. With the advent of time, newer and newer brain
diseases are being discovered. Thus, because of the variability of brain diseases, existing …
diseases are being discovered. Thus, because of the variability of brain diseases, existing …
Mobile health in remote patient monitoring for chronic diseases: Principles, trends, and challenges
Chronic diseases are becoming more widespread. Treatment and monitoring of these
diseases require going to hospitals frequently, which increases the burdens of hospitals and …
diseases require going to hospitals frequently, which increases the burdens of hospitals and …
Automatic detection of Alzheimer's disease progression: An efficient information fusion approach with heterogeneous ensemble classifiers
Predicting Alzheimer's disease (AD) progression is crucial for improving the management of
this chronic disease. Usually, data from AD patients are multimodal and time series in …
this chronic disease. Usually, data from AD patients are multimodal and time series in …
Artificial intelligence-driven prediction modeling and decision making in spine surgery using hybrid machine learning models
Healthcare systems worldwide generate vast amounts of data from many different sources.
Although of high complexity for a human being, it is essential to determine the patterns and …
Although of high complexity for a human being, it is essential to determine the patterns and …
A difference attention ResNet-LSTM network for epileptic seizure detection using EEG signal
X Qiu, F Yan, H Liu - Biomedical Signal Processing and Control, 2023 - Elsevier
Epileptic seizures can affect the patient's physical function and cause irreversible damage to
their brain. It is vital to detect epilepsy seizures in time and give patients antiepileptic …
their brain. It is vital to detect epilepsy seizures in time and give patients antiepileptic …
Prediction of Alzheimer's progression based on multimodal deep-learning-based fusion and visual explainability of time-series data
Alzheimer's disease (AD) is a neurological illness that causes cognitive impairment and has
no known treatment. The premise for delivering timely therapy is the early diagnosis of AD …
no known treatment. The premise for delivering timely therapy is the early diagnosis of AD …