ArticleJournal of the American Medical Informatics Association : JAMIA
Using statistical text classification to identify health information technology incidents.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed.
- A hybrid model integrating RoBERTa, TF-IDF, and attention mechanism for medical query intent classification.Scientific reports · 2025Article
- Assessing the transferability of BERT to patient safety: classifying multiple types of incident reports.BMJ health & care informatics · 2025Article
- Identification of Online Health Information Using Large Pretrained Language Models: Mixed Methods Study.Journal of medical Internet research · 2025Article
- Entity-enhanced BERT for medical specialty prediction based on clinical questionnaire data.PloS one · 2025Article
- Using tumor habitat-derived radiomic analysis during pretreatmentCancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Large Language Models in Neurology Research and Future Practice.Neurology · 2023Article
- Can a Machine Ace the Test? Assessing GPT-4.0's Precision in Plastic Surgery Board Examinations.Plastic and reconstructive surgery. Global open · 2023Article
- Article
- Prediction of Drug-Induced Long QT Syndrome Using Machine Learning Applied to Harmonized Electronic Health Record Data.Journal of cardiovascular pharmacology and therapeutics · 2021Article
- Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review.JMIR medical informatics · 2020Review
- Current challenges in health information technology-related patient safety.Health informatics journal · 2020Article
- Assessment of a Machine Learning Model Applied to Harmonized Electronic Health Record Data for the Prediction of Incident Atrial Fibrillation.JAMA network open · 2020Observational
- Self-Supervised Contextual Language Representation of Radiology Reports to Improve the Identification of Communication Urgency.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Using convolutional neural networks to identify patient safety incident reports by type and severity.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Assisting radiologists with reporting urgent findings to referring physicians: A machine learning approach to identify cases for prompt communication.Journal of biomedical informatics · 2019Article
- Exploration and Initial Development of Text Classification Models to Identify Health Information Technology Usability-Related Patient Safety Event Reports.Applied clinical informatics · 2019Article
- Building Usability Knowledge for Health Information Technology: A Usability-Oriented Analysis of Incident Reports.Applied clinical informatics · 2019Article
- Applications of machine learning in decision analysis for dose management for dofetilide.PloS one · 2019Article
- Artificial Intelligence Learning Semantics via External Resources for Classifying Diagnosis Codes in Discharge Notes.Journal of medical Internet research · 2017Article
- A Primer to the Structure, Content and Linkage of the FDA's Manufacturer and User Facility Device Experience (MAUDE) Files.EGEMS (Washington, DC) · 2017Article
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
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo examine the feasibility of using statistical text classification to automatically identify health information technology (HIT) incidents in the USA Food and Drug Administration (FDA) Manufacturer and User Facility Device Experience (MAUDE) database.
designWe used a subset of 570 272 incidents including 1534 HIT incidents reported to MAUDE between 1 January 2008 and 1 July 2010. Text classifiers using regularized logistic regression were evaluated with both 'balanced' (50% HIT) and 'stratified' (0.297% HIT) datasets for training, validation, and testing. Dataset preparation, feature extraction, feature selection, cross-validation, classification, performance evaluation, and error analysis were performed iteratively to further improve the classifiers. Feature-selection techniques such as removing short words and stop words, stemming, lemmatization, and principal component analysis were examined. MEASUREMENTS: κ statistic, F1 score, precision and recall.
resultsClassification performance was similar on both the stratified (0.954 F1 score) and balanced (0.995 F1 score) datasets. Stemming was the most effective technique, reducing the feature set size to 79% while maintaining comparable performance. Training with balanced datasets improved recall (0.989) but reduced precision (0.165).
conclusionsStatistical text classification appears to be a feasible method for identifying HIT reports within large databases of incidents. Automated identification should enable more HIT problems to be detected, analyzed, and addressed in a timely manner. Semi-supervised learning may be necessary when applying machine learning to big data analysis of patient safety incidents and requires further investigation.
Indexed as
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.