Estimating probability density of content types for promoting medical records search

Abstract

Disease and symptom in medical records tend to appear in different content types: positive, negative, family history and the others. Traditional information retrieval systems depending on keyword match are often adversely affected by the content types. In this paper, we propose a novel learning approach utilizing the content types as features to improve the medical records search. Particularly, the different contents from the medical records are identified using a Bayesian-based classification method. Then, we introduce our type-based weighting function to take advantage of the content types, in which the weights of the content types are automatically calculated by estimating the probability density functions in the documents. Finally, we evaluate the approach on the TREC 2011 and 2012 Medical Records data sets, in which our experimental results show that our approach is promising and superior.