Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2505515.2507874 | CIKM |
Keywords | Field | DocType |
ranking approach,aforementioned patient ranking approach,main approach,novel supervised approach,relevant medical record,medical record,medical history,different query,medical concept,associated medical record | Data mining,Software deployment,Ranking,Information retrieval,Computer science,Ranking (information retrieval),Medical history,Medical record,Concatenation,Classifier (linguistics) | Conference |
Citations | PageRank | References |
6 | 0.48 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nut Limsopatham | 1 | 172 | 14.86 |
| Craig Macdonald | 2 | 2588 | 178.50 |
| Iadh Ounis | 3 | 3438 | 234.59 |