@conference {citeulike:6926634,
	title = {Improving Medical Information Retrieval with PICO Element Detection},
	booktitle = {Proceedings of the ECIR 2010 Conference},
	year = {2010},
	abstract = {Without a well formulated and structured question, it can be very difficult and time consuming for physicians to identify appro- priate 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), Interven- tion (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.},
	keywords = {health, ir, pico},
	pdf = {http://rali.iro.umontreal.ca/rali/sites/default/files/publis/boudin_ecir10.pdf},
	author = {Florian Boudin and Shi, Lixin and Jian-Yun Nie}
}
