Combining classifiers for robust PICO element detection
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.
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.
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.
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.
- Schardt, C, Adams, M, Owens, T, Keitz, S, Fontelo, P (2007) Utilization of the PICO framework to improve searching PubMed for clinical questions. BMC Medical Informatics and Decision Making 7: pp. 16 CrossRef
- Richardson, WS, Wilson, MC, Nishikawa, J, Hayward, RS (1995) The well-built clinical question: a key to evidence-based decisions. ACP J Club 123: pp. 12-3
- Dawes, M, Pluye, P, Shea, L, Grad, R, Greenberg, A, Nie, JY (2007) The identification of clinically important elements within medical journal abstracts: Patient Population Problem, Exposure Intervention, Comparison, Outcome, Duration and Results (PECODR). Informatics in Primary Care 15: pp. 9-16
- Demner-Fushman, D, Lin, J (2006) Answer extraction, semantic clustering, and extractive summarization for clinical question answering.
- McKnight, L, Srinivasan, P (2003) Categorization of sentence types in medical abstracts.
- Demner-Fushman, D, Lin, J (2007) Answering clinical questions with knowledge-based and statistical techniques. Computational Linguistics 33: pp. 63-103 CrossRef
- Hansen, MJ, Rasmussen, NO, Chung, G (2008) A method of extracting the number of trial participants from abstracts describing randomized controlled trials. Journal of Telemedicine and Telecare 14: pp. 354-358 CrossRef
- Chung, G (2009) Sentence retrieval for abstracts of randomized controlled trials. BMC Medical Informatics and Decision Making 9: pp. 10 CrossRef
- Aronson, A (2001) Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program.
- Rindflesch, T, Fiszman, M (2003) The interaction of domain knowledge and linguistic structure in natural language processing: interpreting hypernymic propositions in biomedical text. Journal of Biomedical Informatics 36: pp. 462-477 CrossRef
- Hripcsak, G, Rothschild, AS (2005) Agreement, the F-Measure, and Reliability in Information Retrieval. J Am Med Inform Assoc 12: pp. 296-298 CrossRef
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1472-6947/10/29/prepub
- Combining classifiers for robust PICO element detection
- Open Access
- Available under Open Access This content is freely available online to anyone, anywhere at any time.
BMC Medical Informatics and Decision Making
- Online Date
- May 2010
- Online ISSN
- BioMed Central
- Additional Links
- Author Affiliations
- 1. DIRO, University of Montreal, CP. 6128, succursale Centre-ville, H3C 3J7, Montreal, Quebec, Canada
- 2. Department of Family Medicine, McGill University, 515 Pine Avenue, H2W 1S4, Montreal, Quebec, Canada