Attention on Personalized Clinical Decision Support System: Federated Learning Approach

Abstract

Health management has become a primary problem as new kinds of diseases and complex symptoms are introduced to a rapidly growing modern society. Building a better and smarter healthcare infrastructure is one of the ultimate goals of a smart city. To the best of our knowledge, neural network models are already employed to assist healthcare professionals in achieving this goal. Typically, training a neural network requires a rich amount of data but heterogeneous and vulnerable properties of clinical data introduce a challenge for the traditional centralized network. Moreover, adding new inputs to a medical database requires re-training an existing model from scratch. To tackle these challenges, we proposed a deep learning-based clinical decision support system trained and managed under a federated learning paradigm. We focused on a novel strategy to guarantee the safety of patient privacy and overcome the risk of cyberattacks while enabling large-scale clinical data mining. As a result, we can leverage rich clinical data for training each local neural network without the need for exchanging the confidential data of patients. Moreover, we implemented the proposed scheme as a sequence-to-sequence model architecture integrating the attention mechanism. Thus, our objective is to provide a personalized clinical decision support system with evolvable characteristics that can deliver accurate solutions and assist healthcare professionals in medical diagnosing.

arXiv commentPublished in IEEE BigComp 2021

References in corpus (5)

• Neural Machine Translation by Jointly Learning to Align and Translate
• TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
• Federated Learning: Challenges, Methods, and Future Directions
• Differential Privacy-enabled Federated Learning for Sensitive Health Data
• Federated Learning for Healthcare Informatics

Cited by in corpus (5)

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