Leveraging user query sessions to improve searching of medical literature.

Shiwen Cheng, Vagelis Hristidis, Michael Weiner

Abstract read

In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Shiwen ChengComputer Science and Engineering, University of California, Riverside, CA;

Vagelis HristidisComputer Science and Engineering, University of California, Riverside, CA;

Michael WeinerCenter of Excellence on Implementing Evidence-Based Practice, Department of Veterans Affairs, Veterans Health Administration, Health Services Research and Development Service HFP 04-148, Indianapolis, IN ; Indiana University Center for Health Services and Outcomes Research, Indianapolis, IN ; Regenstrief Institute, Inc., Indianapolis, IN.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Published reports about searching medical literature do not refer to leveraging the query context, as expressed by previous queries in a session. We aimed to assess novel strategies for context-aware searching, hypothesizing that this would be better than baseline. Building upon methods using term frequency-inverse document frequency, we added extensions such as a function incorporating search results and terms of previous queries, with higher weights for more recent queries. Among 60 medical students generating queries against the TREC 9 benchmark dataset, we assessed recall and mean average precision. For difficult queries, we achieved improvement (27%) in average precision over baseline. Improvements in recall were also seen. Our methods outperformed baseline by 4% to 14% on average. Furthermore, the effectiveness of context-aware search was greater for longer query sessions, which are typically more challenging. In conclusion, leveraging the previous queries in a session improved overall search quality with this biomedical database.

Indexed as

PubMedAlgorithmsInformation Storage and RetrievalSearch EngineSensitivity and Specificity

Identifiers

PMID24551332

PMCPMC3900173

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.