ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2010
Dialect topic modeling for improved consumer medical search.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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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.
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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.
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Who cites it
5 citing papers in PubMed.
- Methodologically grounded semantic analysis of large volume of chilean medical literature data applied to the analysis of medical research funding efficiency in Chile.Journal of biomedical semantics · 2020Article
- Using phrases and document metadata to improve topic modeling of clinical reports.Journal of biomedical informatics · 2016Article
- Redundancy-aware topic modeling for patient record notes.PloS one · 2014Article
- Designing and evaluating a clustering system for organizing and integrating patient drug outcomes in personal health messages.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2012Article
- Query log analysis of an electronic health record search engine.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2011Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Access to health information by consumers is hampered by a fundamental language gap. Current attempts to close the gap leverage consumer oriented health information, which does not, however, have good coverage of slang medical terminology. In this paper, we present a Bayesian model to automatically align documents with different dialects (slang, common and technical) while extracting their semantic topics. The proposed diaTM model enables effective information retrieval, even when the query contains slang words, by explicitly modeling the mixtures of dialects in documents and the joint influence of dialects and topics on word selection. Simulations using consumer questions to retrieve medical information from a corpus of medical documents show that diaTM achieves a 25% improvement in information retrieval relevance by nDCG@5 over an LDA baseline.
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Identifiers
21346955PMC3041409What OpenQuestion holds
Registered trials
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.