  Electronic health records (EHR) systems contain vast amounts of medical
information about patients. These data can be used to train machine learning
models that can predict health status, as well as to help prevent future
diseases or disabilities. However, getting patients' medical data to obtain
well-trained machine learning models is a challenging task. This is because
sharing the patients' medical records is prohibited by law in most countries
due to patients privacy concerns. In this paper, we tackle this problem by
sharing the models instead of the original sensitive data by using the mimic
learning approach. The idea is first to train a model on the original sensitive
data, called the teacher model. Then, using this model, we can transfer its
knowledge to another model, called the student model, without the need to learn
the original data used in training the teacher model. The student model is then
shared to the public and can be used to make accurate predictions. To assess
the mimic learning approach, we have evaluated our scheme using different
medical datasets. The results indicate that the student model mimics the
teacher model performance in terms of prediction accuracy without the need to
access to the patients' original data records.
