  Deep neural networks (DNNs) have been broadly adopted in health risk
prediction to provide healthcare diagnoses and treatments. To evaluate their
robustness, existing research conducts adversarial attacks in the
white/gray-box setting where model parameters are accessible. However, a more
realistic black-box adversarial attack is ignored even though most real-world
models are trained with private data and released as black-box services on the
cloud. To fill this gap, we propose the first black-box adversarial attack
method against health risk prediction models named MedAttacker to investigate
their vulnerability. MedAttacker addresses the challenges brought by EHR data
via two steps: hierarchical position selection which selects the attacked
positions in a reinforcement learning (RL) framework and substitute selection
which identifies substitute with a score-based principle. Particularly, by
considering the temporal context inside EHRs, it initializes its RL position
selection policy by using the contribution score of each visit and the saliency
score of each code, which can be well integrated with the deterministic
substitute selection process decided by the score changes. In experiments,
MedAttacker consistently achieves the highest average success rate and even
outperforms a recent white-box EHR adversarial attack technique in certain
cases when attacking three advanced health risk prediction models in the
black-box setting across multiple real-world datasets. In addition, based on
the experiment results we include a discussion on defending EHR adversarial
attacks.

Machine Learning;Artificial Intelligence;Cryptography and Security