Disease Prediction with a Maximum Entropy Method

TL;DR

This paper introduces a maximum entropy approach for disease risk prediction using ICD-10 coded medical histories, demonstrating superior accuracy and revealing disease relationships through comorbidity analysis.

Contribution

It presents a novel maximum entropy algorithm for disease prediction based on medical history data, with rigorous mathematical derivation and empirical validation.

Findings

Achieves twice the accuracy of traditional methods.

Effectively predicts future disease risks.

Reveals intrinsic disease relationships through comorbidity analysis.

Abstract

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.