Patient-age extraction for clinical reports retrieval

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

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
PublisherSpringer
Pages570-576
Number of pages7
ISBN (Electronic)978-3-319-76941-7
ISBN (Print)978-3-319-76940-0
DOIs
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)
PublisherSpringer
Volume10772 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference40th European Conference on Information Retrieval, ECIR 2018
CountryFrance
CityGrenoble
Period26/03/1829/03/18

Fingerprint

Retrieval
Kernel Methods
Decision Support
Natural Language
Dirichlet
Baseline
Experimental Results
Model
Standards
Text
Concepts
Language
Narrative

Cite this

Ramalho, R., Mourão, A., & Magalhães, J. (2018). Patient-age extraction for clinical reports retrieval. In G. Pasi, B. Piwowarski, L. Azzopardi, & A. Hanbury (Eds.), Advances in Information Retrieval - 40th European Conference on IR Research, ECIR 2018, Proceedings (pp. 570-576). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10772 LNCS). Cham: Springer. https://doi.org/10.1007/978-3-319-76941-7_46
Ramalho, Rúben ; Mourão, André ; Magalhães, João. / Patient-age extraction for clinical reports retrieval. Advances in Information Retrieval - 40th European Conference on IR Research, ECIR 2018, Proceedings. editor / G. Pasi ; B. Piwowarski ; L. Azzopardi ; A. Hanbury. Cham : Springer, 2018. pp. 570-576 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Ramalho, R, Mourão, A & Magalhães, J 2018, Patient-age extraction for clinical reports retrieval. in G Pasi, B Piwowarski, L Azzopardi & A Hanbury (eds), Advances in Information Retrieval - 40th European Conference on IR Research, ECIR 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10772 LNCS, Springer, Cham, pp. 570-576, 40th European Conference on Information Retrieval, ECIR 2018, Grenoble, France, 26/03/18. https://doi.org/10.1007/978-3-319-76941-7_46

Patient-age extraction for clinical reports retrieval. / Ramalho, Rúben; Mourão, André; Magalhães, João.

Advances in Information Retrieval - 40th European Conference on IR Research, ECIR 2018, Proceedings. ed. / G. Pasi; B. Piwowarski; L. Azzopardi; A. Hanbury. Cham : Springer, 2018. p. 570-576 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10772 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - 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.

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Ramalho R, Mourão A, Magalhães J. Patient-age extraction for clinical reports retrieval. In Pasi G, Piwowarski B, Azzopardi L, Hanbury A, editors, Advances in Information Retrieval - 40th European Conference on IR Research, ECIR 2018, Proceedings. Cham: Springer. 2018. p. 570-576. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-76941-7_46