ArticleBMC medical research methodology2022
Prediction of acute appendicitis among patients with undifferentiated abdominal pain at emergency department.
Article in BMC medical research methodology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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12 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- Using natural language processing in emergency medicine health service research: A systematic review and meta-analysis.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2024Pooled it
- Can Artificial Intelligence Revolutionise Surgical Decision-Making for Appendectomy? A Narrative Review.Surgical innovation · 2026Review
- Artificial intelligence for early diagnosis in emergency department.Journal of anesthesia, analgesia and critical care · 2026Review
- Artificial intelligence in acute appendicitis: A comprehensive review of machine learning and deep learning applications.World journal of gastroenterology · 2025Review
- Machine Learning for Personalized Prediction of Electrocardiogram (EKG) Use in Emergency Care.Journal of personalized medicine · 2025Article
- A Systematic Integration of Artificial Intelligence Models in Appendicitis Management: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2025Review
- Examining the Use of Machine Learning Algorithms to Enhance the Pediatric Triaging Approach.Open access emergency medicine : OAEM · 2025Article
- Integrating structured and unstructured data for predicting emergency severity: an association and predictive study using transformer-based natural language processing models.BMC medical informatics and decision making · 2024Article
- Efficacy of automated machine learning models and feature engineering for diagnosis of equivocal appendicitis using clinical and computed tomography findings.Scientific reports · 2024Article
- Chronic Appendicitis: Possible Differential Diagnosis in Patients with Chronic Abdominal Pain.Case reports in surgery · 2024Article
- Value of fecal calprotectin in prediction of acute appendicitis based on a proposed model of machine learning.Ulusal travma ve acil cerrahi dergisi = Turkish journal of trauma & emergency surgery : TJTES · 2023Article
- Enhancing Disease Classification in Abdominal CT Scans through RGB Superposition Methods and 2D Convolutional Neural Networks: A Study of Appendicitis and Diverticulitis.Computational and mathematical methods in medicine · 2023Article
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Authors and funding
8 authors at 5 institutions in 2 countries.
Funding
Abstract
backgroundEarly screening and accurately identifying Acute Appendicitis (AA) among patients with undifferentiated symptoms associated with appendicitis during their emergency visit will improve patient safety and health care quality. The aim of the study was to compare models that predict AA among patients with undifferentiated symptoms at emergency visits using both structured data and free-text data from a national survey.
methodsWe performed a secondary data analysis on the 2005-2017 United States National Hospital Ambulatory Medical Care Survey (NHAMCS) data to estimate the association between emergency department (ED) patients with the diagnosis of AA, and the demographic and clinical factors present at ED visits during a patient's ED stay. We used binary logistic regression (LR) and random forest (RF) models incorporating natural language processing (NLP) to predict AA diagnosis among patients with undifferentiated symptoms.
resultsAmong the 40,441 ED patients with assigned International Classification of Diseases (ICD) codes of AA and appendicitis-related symptoms between 2005 and 2017, 655 adults (2.3%) and 256 children (2.2%) had AA. For the LR model identifying AA diagnosis among adult ED patients, the c-statistic was 0.72 (95% CI: 0.69-0.75) for structured variables only, 0.72 (95% CI: 0.69-0.75) for unstructured variables only, and 0.78 (95% CI: 0.76-0.80) when including both structured and unstructured variables. For the LR model identifying AA diagnosis among pediatric ED patients, the c-statistic was 0.84 (95% CI: 0.79-0.89) for including structured variables only, 0.78 (95% CI: 0.72-0.84) for unstructured variables, and 0.87 (95% CI: 0.83-0.91) when including both structured and unstructured variables. The RF method showed similar c-statistic to the corresponding LR model.
conclusionsWe developed predictive models that can predict the AA diagnosis for adult and pediatric ED patients, and the predictive accuracy was improved with the inclusion of NLP elements and approaches.
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