Learning across diverse biomedical data modalities and cohorts: Challenges and opportunities for innovation
In healthcare, machine learning (ML) shows significant potential to augment patient care,
improve population health, and streamline healthcare workflows. Realizing its full potential …
improve population health, and streamline healthcare workflows. Realizing its full potential …
Improving Pancreatic Cyst Management: Artificial Intelligence-Powered Prediction of Advanced Neoplasms through Endoscopic Ultrasound-Guided Confocal …
Despite the increasing rate of detection of incidental pancreatic cystic lesions (PCLs),
current standard-of-care methods for their diagnosis and risk stratification remain …
current standard-of-care methods for their diagnosis and risk stratification remain …
MERGE: A model for multi-input biomedical federated learning
Driven by the deep learning (DL) revolution, artificial intelligence (AI) has become a
fundamental tool for many biomedical tasks, including analyzing and classifying diagnostic …
fundamental tool for many biomedical tasks, including analyzing and classifying diagnostic …
Patchwork learning: A paradigm towards integrative analysis across diverse biomedical data sources
Machine learning (ML) in healthcare presents numerous opportunities for enhancing patient
care, population health, and healthcare providers' workflows. However, the real-world …
care, population health, and healthcare providers' workflows. However, the real-world …
Recent methodological advances in federated learning for healthcare
For healthcare datasets, it is often impossible to combine data samples from multiple sites
due to ethical, privacy, or logistical concerns. Federated learning allows for the utilization of …
due to ethical, privacy, or logistical concerns. Federated learning allows for the utilization of …
A 30-Year Review on Nanocomposites: Comprehensive Bibliometric Insights into Microstructural, Electrical, and Mechanical Properties Assisted by Artificial …
F Gomes Souza Jr, S Bhansali, K Pal… - Materials, 2024 - mdpi.com
From 1990 to 2024, this study presents a groundbreaking bibliometric and sentiment
analysis of nanocomposite literature, distinguishing itself from existing reviews through its …
analysis of nanocomposite literature, distinguishing itself from existing reviews through its …
Multidisciplinary cancer disease classification using adaptive FL in healthcare industry 5.0
Emerging Industry 5.0 designs promote artificial intelligence services and data-driven
applications across multiple places with varying ownership that need special data protection …
applications across multiple places with varying ownership that need special data protection …
Intelligent explainable optical sensing on Internet of nanorobots for disease detection
N Mesgaribarzi, Y Djenouri, AN Belbachir… - Nanotechnology …, 2024 - degruyter.com
Combining deep learning (DL) with nanotechnology holds promise for transforming key
facets of nanoscience and technology. This synergy could pave the way for groundbreaking …
facets of nanoscience and technology. This synergy could pave the way for groundbreaking …
From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare
Federated learning holds great potential for enabling large-scale healthcare research and
collaboration across multiple centres while ensuring data privacy and security are not …
collaboration across multiple centres while ensuring data privacy and security are not …
[PDF][PDF] Sepsis Prediction Using CNNBDLSTM and Temporal Derivatives Feature Extraction in the IoT Medical Environment
Background: Sepsis, a potentially fatal inflammatory disease triggered by infection, carries
significant health implications worldwide. Timely detection is crucial as sepsis can rapidly …
significant health implications worldwide. Timely detection is crucial as sepsis can rapidly …