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.

构建更好、更智能的医疗基础设施是智慧城市的终极目标之一。为了克服临床数据的异构性和易受攻击的特点带来的挑战，我们提出了一个基于深度学习的临床决策支持系统，采用联邦学习范式进行训练和管理，以保证患者隐私的安全，并实现大规模的临床数据挖掘。同时，我们采用了序列到序列模型架构，融合了注意力机制，旨在提供个性化的临床决策支持系统，具有可进化的特性，能够提供准确的解决方案，协助医护人员进行医学诊断。