Comparing the performance of database selection algorithms
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
The effectiveness of query expansion for distributed information retrieval
Proceedings of the tenth international conference on Information and knowledge management
Using sampled data and regression to merge search engine results
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
The effect of topic set size on retrieval experiment error
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic models of information retrieval based on measuring the divergence from randomness
ACM Transactions on Information Systems (TOIS)
The Journal of Machine Learning Research
Retrieval evaluation with incomplete information
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Simple BM25 extension to multiple weighted fields
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Choosing document structure weights
Information Processing and Management: an International Journal
Formal models for expert finding in enterprise corpora
SIGIR '06 Proceedings of the 29th 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
Building enriched document representations using aggregated anchor text
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Multinomial randomness models for retrieval with document fields
ECIR'07 Proceedings of the 29th European conference on IR research
Central-rank-based collection selection in uncooperative distributed information retrieval
ECIR'07 Proceedings of the 29th European conference on IR research
Using relevance feedback in expert search
ECIR'07 Proceedings of the 29th European conference on IR research
Exploiting site-level information to improve web search
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Enhancing electronic medical record retrieval through semantic query expansion
Information Systems and e-Business Management
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
An adaptive evidence weighting method for medical record search
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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Searching medical records is challenging due to their inherent implicit knowledge --- such knowledge may be known by medical practitioners, but it is hidden from an information retrieval (IR) system. For example, it is intuitive for a medical practitioner to assert that patients with heart disease are likely to have records from the hospital's cardiology department. Hence, we hypothesise that this implicit knowledge can be used to enhance a medical records search system that ranks patients based on the relevance of their medical records to a query. In this paper, we propose to group aggregates of medical records from individual hospital departments, which we refer to as department-level evidence, to capture some of the implicit knowledge. In particular, each department-level aggregate consists of all of the medical records created by a particular hospital department, which is then exploited to enhance retrieval effectiveness. Specifically, we propose two approaches to build the department-level evidence based on a federated search and a voting paradigm, respectively. In addition, we introduce an extended voting technique that could leverage this department-level evidence while ranking. We evaluate the retrieval effectiveness of our approaches in the context of the TREC 2011 Medical Records track. Our results show that modelling department-level evidence of records in medical records search improves retrieval effectiveness. In particular, our proposed approach to leverage department-level evidence built using a voting technique obtains results comparable to the best submitted TREC 2011 Medical Records track systems without requiring any external resources that are exploited in those systems.