SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Voting for candidates: adapting data fusion techniques for an expert search task
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Information Systems
Effective pre-retrieval query performance prediction using similarity and variability evidence
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Parallel boosted regression trees for web search ranking
Proceedings of the 20th international conference on World wide web
Bagging gradient-boosted trees for high precision, low variance ranking models
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Exploiting term dependence while handling negation in medical search
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Combining multi-level evidence for medical record retrieval
Proceedings of the 2012 international workshop on Smart health and wellbeing
A task-specific query and document representation for medical records search
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Learning to combine representations for medical records search
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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Two main approaches have emerged in the literature for the effective deployment of a search system to rank patients having a medical history relevant to a query. The first approach is to directly rank patients based on the relevance of their medical history, represented as a concatenation of their associated medical records. Instead, the second approach initially retrieves the relevant medical records of patients, and then ranks the patients based on the relevance of their retrieved medical records. However, these two approaches may be useful for different queries. In this work, we propose a novel supervised approach that can effectively identify when to use either of the two aforementioned patient ranking approaches to attain effective retrieval performance. In particular, our approach deploys a classifier to learn to select a ranking approach when ranking patients, by using query difficulty measures, such as query performance predictors and the number of medical concepts detected in a query, as learning features. We thoroughly evaluate our approach using the standard test collections provided by the TREC Medical Records track. Our results show significant improvements over existing strong baselines.