Demographical Priors for Health Conditions Diagnosis Using Medicare Data
This work addresses improving automated diagnosis systems for healthcare by incorporating demographic data, but it is incremental as it focuses on age-based clustering without major methodological breakthroughs.
The paper investigates how patient age influences susceptibility to medical conditions, showing that age can aid diagnosis by revealing clusters of conditions with distinctive age densities in a dataset of 1.7 million patients and 47 million records from Brazil.
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.