Patient-age extraction for clinical reports retrieval

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationAdvances in Information Retrieval - 40th European Conference on IR Research, ECIR 2018, Proceedings
EditorsG. Pasi, B. Piwowarski, L. Azzopardi, A. Hanbury
Place of PublicationCham
Number of pages7
ISBN (Electronic)978-3-319-76941-7
ISBN (Print)978-3-319-76940-0
Publication statusPublished - 1 Jan 2018
Event40th European Conference on Information Retrieval, ECIR 2018 - Grenoble, France
Duration: 26 Mar 201829 Mar 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10772 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference40th European Conference on Information Retrieval, ECIR 2018


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