Demographical Priors for Health Conditions Diagnosis Using Medicare Data

TL;DR

This study demonstrates that incorporating demographic data, specifically age, alongside symptoms can improve the accuracy of diagnosing health conditions using large-scale electronic health records from Brazil.

Contribution

The paper introduces a method to utilize demographic priors, such as age distributions, to enhance health condition diagnosis from EHR data, revealing distinct age-related clusters of medical conditions.

Findings

Medical conditions form age-specific clusters.

Age information improves diagnostic predictions.

Large-scale Brazilian EHR data supports demographic-based diagnosis.

Abstract

This paper presents an example of how demographical characteristics of patients influence their susceptibility to certain medical conditions. In this paper, we investigate the association of health conditions to age of patients in a heterogeneous population. We show that besides the symptoms a patients is having, the age has the potential of aiding the diagnostic process in hospitals. Working with Electronic Health Records (EHR), we show that medical conditions group into clusters that share distinctive population age densities. We use Electronic Health Records from Brazil for a period of 15 months from March of 2013 to July of 2014. The number of patients in the data is 1.7 million patients and the number of records is 47 million records. The findings has the potential of helping in a setting where an automated system undergoes the task of predicting the condition of a patient given their symptoms and demographical information.