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Open Access Research article

Combining classifiers for robust PICO element detection

Florian Boudin1*, Jian-Yun Nie1, Joan C Bartlett, Roland Grad, Pierre Pluye and Martin Dawes2

Author Affiliations

1 DIRO, University of Montreal, CP. 6128, succursale Centre-ville, Montreal, H3C 3J7 Quebec, Canada

2 Department of Family Medicine, McGill University, 515 Pine Avenue, Montreal, H2W 1S4 Quebec, Canada

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BMC Medical Informatics and Decision Making 2010, 10:29  doi:10.1186/1472-6947-10-29

Published: 15 May 2010

Abstract

Background

Formulating a clinical information need in terms of the four atomic parts which are Population/Problem, Intervention, Comparison and Outcome (known as PICO elements) facilitates searching for a precise answer within a large medical citation database. However, using PICO defined items in the information retrieval process requires a search engine to be able to detect and index PICO elements in the collection in order for the system to retrieve relevant documents.

Methods

In this study, we tested multiple supervised classification algorithms and their combinations for detecting PICO elements within medical abstracts. Using the structural descriptors that are embedded in some medical abstracts, we have automatically gathered large training/testing data sets for each PICO element.

Results

Combining multiple classifiers using a weighted linear combination of their prediction scores achieves promising results with an f-measure score of 86.3% for P, 67% for I and 56.6% for O.

Conclusions

Our experiments on the identification of PICO elements showed that the task is very challenging. Nevertheless, the performance achieved by our identification method is competitive with previously published results and shows that this task can be achieved with a high accuracy for the P element but lower ones for I and O elements.