ArticleJournal of biomedical informatics2008
Infobuttons and classification models: a method for the automatic selection of on-line information resources to fulfill clinicians' information needs.
Article in Journal of biomedical informatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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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
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Optimization of infobutton design and Implementation: A systematic review.Journal of biomedical informatics · 2017Pooled it
- Effectiveness of topic-specific infobuttons: a randomized controlled trial.Journal of the American Medical Informatics Association : JAMIATrial
- Machine Learning Operations in Health Care: A Scoping Review.Mayo Clinic proceedings. Digital health · 2024Review
- Information seeking behavioural paths of physicians for diabetes mellitus care: a qualitative comparative analysis of information needs, sources, and barriers.Health systems (Basingstoke, England) · 2018Article
- Feature engineering and a proposed decision-support system for systematic reviewers of medical evidence.PloS one · 2014Article
- Practical choices for infobutton customization: experience from four sites.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2013Article
- Classification models for the prediction of clinicians' information needs.Journal of biomedical informatics · 2009Article
- Clinician use and acceptance of population-based data about respiratory pathogens: implications for enhancing population-based clinical practice.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2008Article
- Automatically extracting sentences from Medline citations to support clinicians' information needs.Journal of the American Medical Informatics Association : JAMIAArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
objectiveInfobuttons are decision support tools that offer links to information resources based on the context of the interaction between a clinician and an electronic medical record (EMR) system. The objective of this study was to explore machine learning and web usage mining methods to produce classification models for the prediction of information resources that might be relevant in a particular infobutton context.
designClassification models were developed and evaluated with an infobutton usage dataset. The performance of the models was measured and compared with a reference implementation in a series of experiments. MEASUREMENTS: Level of agreement (kappa) between the models and the resources that clinicians actually used in each infobutton session.
resultsThe classification models performed significantly better than the reference implementation (p<.0001). The performance of these models tended to decrease over time, probably due to a phenomenon known as concept drift. However, the performance of the models remained stable when concept drift handling techniques were used.
conclusionsThe results suggest that classification models are a promising method for the prediction of information resources that a clinician would use to answer patient care questions.
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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.