ArticleJournal of the American Medical Informatics Association : JAMIA2013
Developing and evaluating an automated appendicitis risk stratification algorithm for pediatric patients in the emergency department.
Article in Journal of the American Medical Informatics Association : JAMIA, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 54 citations in OpenAlex.
- Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review.Artificial intelligence in medicine · 2023Pooled it
- Automated, machine learning-based alerts increase epilepsy surgery referrals: A randomized controlled trial.Epilepsia · 2023Trial
- An Evidence-Based Framework for Patient-Facing Artificial Intelligence Integration in the Emergency Department.Journal of the American College of Emergency Physicians open · 2026Article
- Research on the construction of an artificial intelligence model for the early diagnosis of pediatric acute appendicitis: a single-center retrospective study based on complete blood count.BMC pediatrics · 2026Article
- Artificial intelligence in acute appendicitis: A comprehensive review of machine learning and deep learning applications.World journal of gastroenterology · 2025Review
- AI-Driven Injury Reporting in Pediatric Emergency Departments.JAMA network open · 2025Article
- External validation of predictive models for diagnosis, management and severity of pediatric appendicitis.Frontiers in pediatrics · 2025Article
- Implementation of Machine Learning Pipelines for Clinical Practice: Development and Validation Study.JMIR medical informatics · 2022Article
- Automated Pulmonary Embolism Risk Assessment Using the Wells Criteria: Validation Study.JMIR formative research · 2022Article
- Machine Learning and Syncope Management in the ED: The Future Is Coming.Medicina (Kaunas, Lithuania) · 2021Review
- Using Machine Learning to Predict the Diagnosis, Management and Severity of Pediatric Appendicitis.Frontiers in pediatrics · 2021Article
- Artificial intelligence in emergency medicine: A scoping review.Journal of the American College of Emergency Physicians open · 2020Article
- Role of Artificial Intelligence in Fighting Antimicrobial Resistance in Pediatrics.Antibiotics (Basel, Switzerland) · 2020Review
- Article
- Comparison of orthogonal NLP methods for clinical phenotyping and assessment of bone scan utilization among prostate cancer patients.Journal of biomedical informatics · 2019Article
- Quantifying risk factors in medical reports with a context-aware linear model.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Data Challenges With Real-Time Safety Event Detection And Clinical Decision Support.Journal of medical Internet research · 2019Article
- Extraction of Geriatric Syndromes From Electronic Health Record Clinical Notes: Assessment of Statistical Natural Language Processing Methods.JMIR medical informatics · 2019Article
- Applications of Machine Learning Approaches in Emergency Medicine; a Review Article.Archives of academic emergency medicine · 2019Review
- Automated versus Manual Data Extraction of the Padua Prediction Score for Venous Thromboembolism Risk in Hospitalized Older Adults.Applied clinical informatics · 2018Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
objectiveTo evaluate a proposed natural language processing (NLP) and machine-learning based automated method to risk stratify abdominal pain patients by analyzing the content of the electronic health record (EHR).
methodsWe analyzed the EHRs of a random sample of 2100 pediatric emergency department (ED) patients with abdominal pain, including all with a final diagnosis of appendicitis. We developed an automated system to extract relevant elements from ED physician notes and lab values and to automatically assign a risk category for acute appendicitis (high, equivocal, or low), based on the Pediatric Appendicitis Score. We evaluated the performance of the system against a manually created gold standard (chart reviews by ED physicians) for recall, specificity, and precision.
resultsThe system achieved an average F-measure of 0.867 (0.869 recall and 0.863 precision) for risk classification, which was comparable to physician experts. Recall/precision were 0.897/0.952 in the low-risk category, 0.855/0.886 in the high-risk category, and 0.854/0.766 in the equivocal-risk category. The information that the system required as input to achieve high F-measure was available within the first 4 h of the ED visit.
conclusionsAutomated appendicitis risk categorization based on EHR content, including information from clinical notes, shows comparable performance to physician chart reviewers as measured by their inter-annotator agreement and represents a promising new approach for computerized decision support to promote application of evidence-based medicine at the point of care.
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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.