Journal of Biomedical Informatics - Special issue: Unified medical language system
Answering Clinical Questions with Knowledge-Based and Statistical Techniques
Computational Linguistics
Positional language models for clinical information retrieval
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Recent developments in information retrieval
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Semantic concept-enriched dependence model for medical information retrieval
Journal of Biomedical Informatics
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Without a well formulated and structured question, it can be very difficult and time consuming for physicians to identify appropriate resources and search for the best available evidence for medical treatment in evidence-based medicine (EBM). In EBM, clinical studies and questions involve four aspects: Population/Problem (P), Intervention (I), Comparison (C) and Outcome (O), which are known as PICO elements. It is intuitively more advantageous to use these elements in Information Retrieval (IR). In this paper, we first propose an approach to automatically identify the PICO elements in documents and queries. We test several possible approaches to use the identified elements in IR. Experiments show that it is a challenging task to determine accurately PICO elements. However, even with noisy tagging results, we can still take advantage of some PICO elements, namely I and P elements, to enhance the retrieval process, and this allows us to obtain significantly better retrieval effectiveness than the state-of-the-art methods.