Attention on Personalized Clinical Decision Support System: Federated Learning Approach
This addresses the problem of privacy and data security in healthcare for medical professionals, but it is incremental as it combines existing federated learning and attention mechanisms in a new application.
The paper tackled the challenges of training neural networks with heterogeneous and vulnerable clinical data by proposing a federated learning-based clinical decision support system, which leverages rich data without exchanging confidential patient information and integrates an attention mechanism for personalized and evolvable medical diagnosing assistance.
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.