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 …
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 …
Artificial intelligence and biosensors in healthcare and its clinical relevance: A review
Data generated from sources such as wearable sensors, medical imaging, personal health
records, and public health organizations have resulted in a massive information increase in …
records, and public health organizations have resulted in a massive information increase in …
[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] 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 …
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 …
Occupational injury risk mitigation: machine learning approach and feature optimization for smart workplace surveillance
Forecasting the severity of occupational injuries shall be all industries' top priority. The use
of machine learning is theoretically valuable to assist the predictive analysis, thus, this study …
of machine learning is theoretically valuable to assist the predictive analysis, thus, this study …
[HTML][HTML] Multimodal representations of biomedical knowledge from limited training whole slide images and reports using deep learning
The increasing availability of biomedical data creates valuable resources for develo**
new deep learning algorithms to support experts, especially in domains where collecting …
new deep learning algorithms to support experts, especially in domains where collecting …
[HTML][HTML] Machine learning in cardiology: Clinical application and basic research
Machine learning is a subfield of artificial intelligence. The quality and versatility of machine
learning have been rapidly improving and playing a critical role in many aspects of social …
learning have been rapidly improving and playing a critical role in many aspects of social …