ArticleApplied clinical informatics2017
Using Active Learning to Identify Health Information Technology Related Patient Safety Events.
Article in Applied clinical informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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.
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.
Who cites it
5 citing papers in PubMed.
- Risk Management and Patient Safety in the Artificial Intelligence Era: A Systematic Review.Healthcare (Basel, Switzerland) · 2024Review
- Use of machine learning in geriatric clinical care for chronic diseases: a systematic literature review.JAMIA open · 2020Review
- An Evidence-Based Tool for Safe Configuration of Electronic Health Records: The eSafety Checklist.Applied clinical informatics · 2018Article
- Review
- Engineering and clinical use of artificial intelligence (AI) with machine learning and data science advancements: radiology leading the way for future.Therapeutic advances in urologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The widespread adoption of health information technology (HIT) has led to new patient safety hazards that are often difficult to identify. Patient safety event reports, which are self-reported descriptions of safety hazards, provide one view of potential HIT-related safety events. However, identifying HIT-related reports can be challenging as they are often categorized under other more predominate clinical categories. This challenge of identifying HIT-related reports is exacerbated by the increasing number and complexity of reports which pose challenges to human annotators that must manually review reports. In this paper, we apply active learning techniques to support classification of patient safety event reports as HIT-related. We evaluated different strategies and demonstrated a 30% increase in average precision of a confirmatory sampling strategy over a baseline no active learning approach after 10 learning iterations.
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Identifiers
What 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.