Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine Learning with Electronic Health Records is vulnerable to
  Backdoor Trigger Attacks   Electronic Health Records (EHRs) provide a wealth of information for machine
learning algorithms to predict the patient outcome from the data including
diagnostic information, vital signals, lab tests, drug administration, and
demographic information. Machine learning models can be built, for example, to
evaluate patients based on their predicted mortality or morbidity and to
predict required resources for efficient resource management in hospitals. In
this paper, we demonstrate that an attacker can manipulate the machine learning
predictions with EHRs easily and selectively at test time by backdoor attacks
with the poisoned training data. Furthermore, the poison we create has
statistically similar features to the original data making it hard to detect,
and can also attack multiple machine learning models without any knowledge of
the models. With less than 5% of the raw EHR data poisoned, we achieve average
attack success rates of 97% on mortality prediction tasks with MIMIC-III
database against Logistic Regression, Multilayer Perceptron, and Long
Short-term Memory models simultaneously.

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The categories of this paper are: physics
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
15
The categories of this paper are: nucl-ex
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
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The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
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The categories of this paper are: eess
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The categories of this paper are: acc-phys
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The categories of this paper are: astro-ph
18
The categories of this paper are: econ
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The categories of this paper are: comp-gas
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The categories of this paper are: dg-ga
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The categories of this paper are: adap-org