ArticleBMC medical informatics and decision making2017
Using multiclass classification to automate the identification of patient safety incident reports by type and severity.
Article in BMC medical informatics and decision making, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The use of natural language processing in detecting and predicting falls within the healthcare setting: a systematic review.International journal for quality in health care : journal of the International Society for Quality in Health Care · 2023Pooled it
- Machine Learning to Prioritize High-Severity Patient Safety Events for Institutional Investigation: Algorithm Development and Validation Study.JMIR medical informatics · 2026Article
- Using Artificial Intelligence to Identify Patterns and Predictors of Adverse Drug Reactions in Cardiovascular Patients: A Comprehensive Narrative Review.American journal of cardiovascular drugs : drugs, devices, and other interventions · 2026Review
- Development and validation of a machine learning-based clinical decision support tool for stratifying intravenous medication risk in hospitalized patients with heart failure.International journal of clinical pharmacy · 2026Article
- Article
- Assessing the transferability of BERT to patient safety: classifying multiple types of incident reports.BMJ health & care informatics · 2025Article
- Evaluating Active Learning Strategies for Automated Classification of Patient Safety Event Reports in Hospitals.Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting · 2024Article
- Construction of a Multi-Label Classifier for Extracting Multiple Incident Factors From Medication Incident Reports in Residential Care Facilities: Natural Language Processing Approach.JMIR medical informatics · 2024Article
- A large dataset of annotated incident reports on medication errors.Scientific data · 2024Article
- Risk Management and Patient Safety in the Artificial Intelligence Era: A Systematic Review.Healthcare (Basel, Switzerland) · 2024Review
- A Machine Learning Approach with Human-AI Collaboration for Automated Classification of Patient Safety Event Reports: Algorithm Development and Validation Study.JMIR human factors · 2024Article
- Using Healthcare Resources Wisely: A Predictive Support System Regarding the Severity of Patient Falls.Journal of healthcare engineering · 2022Article
- Evaluating resampling methods and structured features to improve fall incident report identification by the severity level.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Multi-feature fusion framework for sarcasm identification on twitter data: A machine learning based approach.PloS one · 2021Article
- Use of machine learning in geriatric clinical care for chronic diseases: a systematic literature review.JAMIA open · 2020Review
- Can Unified Medical Language System-based semantic representation improve automated identification of patient safety incident reports by type and severity?Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Automatic Incident Triage in Radiation Oncology Incident Learning System.Healthcare (Basel, Switzerland) · 2020Article
- Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review.JMIR medical informatics · 2020Review
- Clinical Text Data in Machine Learning: Systematic Review.JMIR medical informatics · 2020Review
- Current challenges in health information technology-related patient safety.Health informatics journal · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundApproximately 10% of admissions to acute-care hospitals are associated with an adverse event. Analysis of incident reports helps to understand how and why incidents occur and can inform policy and practice for safer care. Unfortunately our capacity to monitor and respond to incident reports in a timely manner is limited by the sheer volumes of data collected. In this study, we aim to evaluate the feasibility of using multiclass classification to automate the identification of patient safety incidents in hospitals.
methodsText based classifiers were applied to identify 10 incident types and 4 severity levels. Using the one-versus-one (OvsO) and one-versus-all (OvsA) ensemble strategies, we evaluated regularized logistic regression, linear support vector machine (SVM) and SVM with a radial-basis function (RBF) kernel. Classifiers were trained and tested with "balanced" datasets (n_
resultsThe most effective combination was a OvsO ensemble of binary SVM RBF classifiers with binary count feature extraction. For incident type, classifiers performed well on balanced and stratified datasets (F-score: 78.3, 73.9%), but were worse on independent datasets (68.5%). Reports about falls, medications, pressure injury, aggression and blood products were identified with high recall and precision. "Documentation" was the hardest type to identify. For severity level, F-score for severity assessment code (SAC) 1 (extreme risk) was 87.3 and 64% for SAC4 (low risk) on balanced data. With stratified data, high recall was achieved for SAC1 (82.8-84%) but precision was poor (6.8-11.2%). High risk incidents (SAC2) were confused with medium risk incidents (SAC3).
conclusionsBinary classifier ensembles appear to be a feasible method for identifying incidents by type and severity level. Automated identification should enable safety problems to be detected and addressed in a more timely manner. Multi-label classifiers may be necessary for reports that relate to more than one incident type.
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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.