Deep Learning for Personalized Healthcare Services

2021 ◽  
Keyword(s):  
Deep Learning ◽  
Healthcare Services ◽  
Personalized Healthcare

scholarly journals Federated Deep Learning Architecture for Personalized Healthcare

2021 ◽  
Author(s):  
Helen Chen ◽  
Shubhankar Mohapatra ◽  
George Michalopoulos ◽  
Xi He ◽  
Ian McKillop
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Modeling ◽  
Privacy Preserving ◽  
Security And Privacy ◽  
Neural Network Modeling ◽  
Network Architectures ◽  
Personalized Healthcare ◽  
Face To Face ◽  
Learning Architectures

Using deep learning to advance personalized healthcare requires data about patients to be collected and aggregated from disparate sources that often span institutions and geographies. Researchers regularly come face-to-face with legitimate security and privacy policies that constrain access to these data. In this work, we present a vision for privacy-preserving federated neural network architectures that permit data to remain at a custodian’s institution while enabling the data to be discovered and used in neural network modeling. Using a diabetes dataset, we demonstrate that accuracy and processing efficiencies using federated deep learning architectures are equivalent to the models built on centralized datasets.

scholarly journals Deep Neural Networks on Mobile Healthcare Applications: Practical Recommendations

Proceedings ◽  
2018 ◽  
Vol 2 (19) ◽  
pp. 550 ◽  
Author(s):  
Jose I. Benedetto ◽  
Pablo Sanabria ◽  
Andres Neyem ◽  
Jaime Navon ◽  
Christian Poellabauer ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Brain Injuries ◽  
Network Connectivity ◽  
Medical Application ◽  
Healthcare Services ◽  
Mobile Environment ◽  
Mobile Platforms ◽  
Healthcare Applications

Deep learning has for a long time been recognized as a powerful tool in the field of medicine for making predictions or detecting abnormalities in a patient’s data. However, up until recently, hosting of these neural networks has been relegated to the domain of servers and powerful workstations due to the vast amount of resources they require. This trend has been steadily shifting in the recent years, and we are now beginning to see more and more mobile applications with similar capabilities. Deep neural networks hosted completely on mobile platforms are extremely valuable for providing healthcare services to remote areas without network connectivity. Yet despite this, there is very little information regarding the migration process of an existing server-based neural network to a mobile environment. In this work, we describe the various techniques and considerations that should be taken into account when developing a deep-learning enabled mobile application with offline support. We illustrate the above by providing a concrete example through our experience in migrating to mobile an in-house developed medical application for detecting early signs of traumatic brain injuries.

scholarly journals A Smart Biometric Identity Management Framework for Personalised IoT and Cloud Computing-Based Healthcare Services

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 552
Author(s):  
Farnaz Farid ◽  
Mahmoud Elkhodr ◽  
Fariza Sabrina ◽  
Farhad Ahamed ◽  
Ergun Gide
Keyword(s):  
Cloud Computing ◽  
Identity Management ◽  
Homomorphic Encryption ◽  
Healthcare Services ◽  
Healthcare Systems ◽  
Security And Privacy ◽  
Management Framework ◽  
Personalized Healthcare ◽  
Authentication Mechanism ◽  
Federated Identity

This paper proposes a novel identity management framework for Internet of Things (IoT) and cloud computing-based personalized healthcare systems. The proposed framework uses multimodal encrypted biometric traits to perform authentication. It employs a combination of centralized and federated identity access techniques along with biometric based continuous authentication. The framework uses a fusion of electrocardiogram (ECG) and photoplethysmogram (PPG) signals when performing authentication. In addition to relying on the unique identification characteristics of the users’ biometric traits, the security of the framework is empowered by the use of Homomorphic Encryption (HE). The use of HE allows patients’ data to stay encrypted when being processed or analyzed in the cloud. Thus, providing not only a fast and reliable authentication mechanism, but also closing the door to many traditional security attacks. The framework’s performance was evaluated and validated using a machine learning (ML) model that tested the framework using a dataset of 25 users in seating positions. Compared to using just ECG or PPG signals, the results of using the proposed fused-based biometric framework showed that it was successful in identifying and authenticating all 25 users with 100% accuracy. Hence, offering some significant improvements to the overall security and privacy of personalized healthcare systems.

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