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Harnessing big data analytics for healthcare: A comprehensive review of frameworks, implications, applications, and impacts
A Ahmed, R ** review of artificial intelligence-based methods for diabetes risk prediction
The increasing prevalence of type 2 diabetes mellitus (T2DM) and its associated health
complications highlight the need to develop predictive models for early diagnosis and …
complications highlight the need to develop predictive models for early diagnosis and …
Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs)
This paper explores the exposome concept and its role in elucidating the interplay between
environmental exposures and human health. We introduce two key concepts critical for …
environmental exposures and human health. We introduce two key concepts critical for …
A review of biosensors and artificial intelligence in healthcare and their clinical significance
In the past decade, a substantial increase in medical data from various sources, including
wearable sensors, medical imaging, personal health records, and public health …
wearable sensors, medical imaging, personal health records, and public health …
[HTML][HTML] Review of multimodal machine learning approaches in healthcare
Abstract Machine learning methods in healthcare have traditionally focused on using data
from a single modality, limiting their ability to effectively replicate the clinical practice of …
from a single modality, limiting their ability to effectively replicate the clinical practice of …
[HTML][HTML] Neural networks for intelligent multilevel control of artificial and natural objects based on data fusion: A survey
Today the tasks of complex artificial and natural objects control have come to the fore in the
majority of subject domains. The efficiency and effectiveness of solving these tasks directly …
majority of subject domains. The efficiency and effectiveness of solving these tasks directly …
Applications of AI in multi-modal imaging for cardiovascular disease
Data for healthcare is diverse and includes many different modalities. Traditional
approaches to Artificial Intelligence for cardiovascular disease were typically limited to …
approaches to Artificial Intelligence for cardiovascular disease were typically limited to …
[HTML][HTML] Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and …
This paper discusses some overlooked challenges faced when working with machine
learning models for histopathology and presents a novel opportunity to support “Learning …
learning models for histopathology and presents a novel opportunity to support “Learning …
[PDF][PDF] Analysis of multi-modal data through deep learning techniques to diagnose CVDs: A review
Abstracts: In cardiology, there has been a surge in artificial intelligence (AI), machine
learning, and deep learning techniques. Artificial intelligence (AI) and electronic health …
learning, and deep learning techniques. Artificial intelligence (AI) and electronic health …