Patient-age extraction for clinical reports retrieval

Abstract

Patient demographics are of great importance in clinical decision processes for both diagnosis, tests and treatments. Natural language is the standard in clinical case reports, however, numerical concepts, such as age, do not show their full potential when treated as text tokens. In this paper, we consider the patient age as a numerical dimension and investigate several Kernel methods to smooth a temporal retrieval model. We extract patient age from the clinical case narrative and extend a Dirichlet language to include the temporal dimension. Experimental results on a clinical decision support task, showed that our proposal achieves a relative improvement of 5.7% at the top 10 retrieved documents over a time agnostic baseline.