  In this paper, we propose a maximum entropy method for predicting disease
risks. It is based on a patient's medical history with diseases coded in ICD-10
which can be used in various cases. The complete algorithm with strict
mathematical derivation is given. We also present experimental results on a
medical dataset, demonstrating that our method performs well in predicting
future disease risks and achieves an accuracy rate twice that of the
traditional method. We also perform a comorbidity analysis to reveal the
intrinsic relation of diseases.
