Abstract
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.
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Limsopatham, N., Macdonald, C., Ounis, I. (2013). Aggregating Evidence from Hospital Departments to Improve Medical Records Search. In: Serdyukov, P., et al. Advances in Information Retrieval. ECIR 2013. Lecture Notes in Computer Science, vol 7814. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36973-5_24
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DOI: https://doi.org/10.1007/978-3-642-36973-5_24
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