ArticleScientific reports2023
Development and evaluation of a clinical decision support system for early diagnosis of acute appendicitis.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Designing Clinical Decision Support Systems (CDSS)-A User-Centered Lens of the Design Characteristics, Challenges, and Implications: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Role of immature granulocyte and blood biomarkers in predicting perforated acute appendicitis using machine learning model.World journal of clinical cases · 2025Article
- Development and evaluation of the musculoskeletal physical therapy registry in Iran: a step toward enhanced evidence-based practice.BMC musculoskeletal disorders · 2025Article
- Design and Evaluation of ACAFiB-APP, a clinical decision support system for anticoagulant considerations in patients with atrial fibrillation.BMC cardiovascular disorders · 2025Article
- Construction of a clinical prediction model for complicated appendicitis based on machine learning techniques.Scientific reports · 2024Article
- Tryptase in Acute Appendicitis: Unveiling Allergic Connections through Compelling Evidence.International journal of molecular sciences · 2024Article
- A methodological study of clustering evaluation in medical data: Exploring internal and stability-based criteria.Digital healthArticle
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The most frequent reason for individuals experiencing abdominal discomfort to be referred to emergency departments of hospitals is acute appendicitis, and the most frequent emergency surgery performed is an appendectomy. The purpose of this study was to design and develop an intelligent clinical decision support system for the timely and accurate diagnosis of acute appendicitis. The number of participants which is equal to 181 was chosen as the sample size for developing and evaluating neural networks. The information was gathered from the medical files of patients who underwent appendicectomies at Shahid Modarres Hospital as well as from the findings of their appendix samples' pathological tests. The diagnostic outcomes were then ascertained by the development and comparison of a Multilayer Perceptron network (MLP) and a Support Vector Machine (SVM) system in the MATLAB environment. The SVM algorithm functioned as the central processing unit in the Clinical Decision Support System (CDSS) that was built. The intelligent appendicitis diagnostic system was subsequently developed utilizing the Java programming language. Technical evaluation and system usability testing were both done as part of the software evaluation process. Comparing the output of the optimized artificial neural network of the SVM with the pathology result showed that the network's sensitivity, specificity, and accuracy were 91.7%, 96.2%, and 95%, respectively, in diagnosing acute appendicitis. Based on the existing standards and the opinions of general surgeons, and also comparing the results with the diagnostic accuracy of general surgeons, findings indicated the proper functioning of the network for the diagnosis of acute appendicitis. The use of this system in medical centers is useful for purposes such as timely diagnosis and prevention of negative appendectomy, reducing patient hospital stays and treatment costs, and improving the patient referral system.
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