ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2015
Incorporating Statistical Topic Models in the Retrieval of Healthcare Documents.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Predicting inpatient clinical order patterns with probabilistic topic models vs conventional order sets.Journal of the American Medical Informatics Association : JAMIA · 2017Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Patients often search for information on the web about treatments and diseases after they are discharged from the hospital. However, searching for medical information on the web poses challenges due to related terms and synonymy for the same disease and treatment. In this paper, we present a method that combines Statistical Topics Models, Language Models and Natural Language Processing to retrieve healthcare related documents. In addition, we test if the incorporation of terms extracted from the patient's discharge summary improves the retrieval performance. We show that the proposed framework outperformed the winner of the retrieval CLEF eHealth 2013 challenge by 68% in the MAP measure (0:5226 vs 0:3108), and by 13% in NDCG (0:5202 vs 0:3637). Compared with standard language models, we obtain an improvement of 92% in MAP (0:2666) and 45% in NDCG. (0:3637).
Identifiers
26306280PMC4525234What OpenQuestion holds
Registered trials
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