Disease Prediction with a Maximum Entropy Method
This method offers a significant improvement in disease risk prediction for medical practitioners and patients, potentially enabling earlier interventions.
This paper proposes a maximum entropy method to predict disease risks based on a patient's medical history. The method achieves an accuracy rate twice that of traditional methods in predicting future disease risks.
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.