ArticleJournal of medical Internet research2017
Recommending Education Materials for Diabetic Questions Using Information Retrieval Approaches.
Article in Journal of medical Internet research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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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
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Information Architecture of Web-Based Interventions to Improve Health Outcomes: Systematic Review.Journal of medical Internet research · 2018Pooled it
- The Unified Medical Language System at 30 Years and How It Is Used and Published: Systematic Review and Content Analysis.JMIR medical informatics · 2021Review
- Using Natural Language Processing Techniques to Provide Personalized Educational Materials for Chronic Disease Patients in China: Development and Assessment of a Knowledge-Based Health Recommender System.JMIR medical informatics · 2020Article
- Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey.Yearbook of medical informatics · 2019Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
backgroundSelf-management is crucial to diabetes care and providing expert-vetted content for answering patients' questions is crucial in facilitating patient self-management.
objectiveThe aim is to investigate the use of information retrieval techniques in recommending patient education materials for diabetic questions of patients.
methodsWe compared two retrieval algorithms, one based on Latent Dirichlet Allocation topic modeling (topic modeling-based model) and one based on semantic group (semantic group-based model), with the baseline retrieval models, vector space model (VSM), in recommending diabetic patient education materials to diabetic questions posted on the TuDiabetes forum. The evaluation was based on a gold standard dataset consisting of 50 randomly selected diabetic questions where the relevancy of diabetic education materials to the questions was manually assigned by two experts. The performance was assessed using precision of top-ranked documents.
resultsWe retrieved 7510 diabetic questions on the forum and 144 diabetic patient educational materials from the patient education database at Mayo Clinic. The mapping rate of words in each corpus mapped to the Unified Medical Language System (UMLS) was significantly different (P<.001). The topic modeling-based model outperformed the other retrieval algorithms. For example, for the top-retrieved document, the precision of the topic modeling-based, semantic group-based, and VSM models was 67.0%, 62.8%, and 54.3%, respectively.
conclusionsThis study demonstrated that topic modeling can mitigate the vocabulary difference and it achieved the best performance in recommending education materials for answering patients' questions. One direction for future work is to assess the generalizability of our findings and to extend our study to other disease areas, other patient education material resources, and online forums.
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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.