OpenClinicalAI: An Open and Dynamic Model for Alzheimer's Disease
  Diagnosis
Yunyou Huang Xiaoshuang Liang Xiangjiang Lu Xiuxia Miao Jiyue Xie Wenjing Liu Fan Zhang Guoxin Kang Li Ma Suqin Tang Zhifei Zhang Jianfeng Zhan
Although Alzheimer's disease (AD) cannot be reversed or cured, timely
diagnosis can significantly reduce the burden of treatment and care. Current
research on AD diagnosis models usually regards the diagnosis task as a typical
classification task with two primary assumptions: 1) All target categories are
known a priori; 2) The diagnostic strategy for each patient is consistent, that
is, the number and type of model input data for each patient are the same.
However, real-world clinical settings are open, with complexity and uncertainty
in terms of both subjects and the resources of the medical institutions. This
means that diagnostic models may encounter unseen disease categories and need
to dynamically develop diagnostic strategies based on the subject's specific
circumstances and available medical resources. Thus, the AD diagnosis task is
tangled and coupled with the diagnosis strategy formulation. To promote the
application of diagnostic systems in real-world clinical settings, we propose
OpenClinicalAI for direct AD diagnosis in complex and uncertain clinical
settings. This is the first powerful end-to-end model to dynamically formulate
diagnostic strategies and provide diagnostic results based on the subject's
conditions and available medical resources. OpenClinicalAI combines
reciprocally coupled deep multiaction reinforcement learning (DMARL) for
diagnostic strategy formulation and multicenter meta-learning (MCML) for
open-set recognition. The experimental results show that OpenClinicalAI
achieves better performance and fewer clinical examinations than the
state-of-the-art model. Our method provides an opportunity to embed the AD
diagnostic system into the current health care system to cooperate with
clinicians to improve current health care.