Clinical information retrieval using document and PICO structure

Authors:
Florian Boudin;Jian-Yun Nie;Martin Dawes
Affiliations:
Université de Montréal, Montreal, Quebec, Canada;Université de Montréal, Montreal, Quebec, Canada;McGill University, Montréal, Québec, Canada
Venue:
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Year:
2010

Citing 8
Cited 5

Effective retrieval of structured documents

SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
A language modeling approach to information retrieval

Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
XIRQL: a query language for information retrieval in XML documents

Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
The interaction of domain knowledge and linguistic structure in natural language processing: interpreting hypernymic propositions in biomedical text

Journal of Biomedical Informatics - Special issue: Unified medical language system
Choosing document structure weights

Information Processing and Management: an International Journal
Structured queries in XML retrieval

Proceedings of the 14th ACM international conference on Information and knowledge management
Answer extraction, semantic clustering, and extractive summarization for clinical question answering

ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
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
Automatic classification of sentences for evidence based medicine

DTMBIO '10 Proceedings of the ACM fourth international workshop on Data and text mining in biomedical informatics
Deriving a test collection for clinical information retrieval from systematic reviews

DTMBIO '10 Proceedings of the ACM fourth international workshop on Data and text mining in biomedical informatics
Inferring appropriate eligibility criteria in clinical trial protocols without labeled data

Proceedings of the ACM sixth international workshop on Data and text mining in biomedical informatics
PICO element detection in medical text without metadata: Are first sentences enough?

Journal of Biomedical Informatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

In evidence-based medicine, clinical questions involve four aspects: Patient/Problem (P), Intervention (I), Comparison (C) and Outcome (O), known as PICO elements. In this paper we present a method that extends the language modeling approach to incorporate both document structure and PICO query formulation. We present an analysis of the distribution of PICO elements in medical abstracts that motivates the use of a location-based weighting strategy. In experiments carried out on a collection of 1.5 million abstracts, the method was found to lead to an improvement of roughly 60% in MAP and 70% in P@10 as compared to state-of-the-art methods.