ArticleObesity surgery2019
A Clinical Decision Support System for Predicting the Early Complications of One-Anastomosis Gastric Bypass Surgery.
Article in Obesity surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 4 of them syntheses that pooled it.
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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.
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Who cites it
19 citing papers in PubMed, 4 syntheses or guidelines pooled it, 33 citations in OpenAlex.
- The Evolution of Bariatric and Metabolic Surgery in the Artificial Intelligence Era: A Comprehensive Systematic Review of Current Applications and Clinical Implications.Obesity surgery · 2026Pooled it
- Dumping Syndrome After One Anastomosis Gastric Bypass-A Systematic Review.Obesity surgery · 2025Pooled it
- Hair Loss and Metabolic and Bariatric Surgery: An Updated Systematic Review and Meta-analysis.Obesity surgery · 2025Pooled it
- Artificial Intelligence in Bariatric Surgery: Current Status and Future Perspectives.Obesity surgery · 2022Pooled it
- Sleeve Gastrectomy With Proximal Jejunal Bypass: Balancing Simplicity and Efficacy in Metabolic and Bariatric Surgery.Journal of metabolic and bariatric surgery · 2026Review
- Multimodal and Multidimensional Artificial Intelligence Technology in Obesity.Journal of obesity & metabolic syndrome · 2025Review
- Integrating Machine Learning and Dynamic Digital Follow-up for Enhanced Prediction of Postoperative Complications in Bariatric Surgery.Obesity surgery · 2025Article
- Predicting pregnancy at the first year following metabolic-bariatric surgery: development and validation of machine learning models.Surgical endoscopy · 2025Article
- Article
- Harnessing Artificial Intelligence in Obesity Research and Management: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2025Review
- Development of the Korean Quality Improvement Platform in Surgery (K-QIPS) program: a nationwide project to improve surgical quality and patient safety.Annals of surgical treatment and research · 2024Article
- Predicting short-term thromboembolic risk following Roux-en-Y gastric bypass using supervised machine learning.World journal of gastrointestinal surgery · 2024Article
- Machine learning-based mortality prediction models for smoker COVID-19 patients.BMC medical informatics and decision making · 2023Article
- The development of machine learning in bariatric surgery.Frontiers in surgery · 2023Review
- Current Applications of Artificial Intelligence in Bariatric Surgery.Obesity surgery · 2022Review
- Machine Learning Models to Predict In-Hospital Mortality among Inpatients with COVID-19: Underestimation and Overestimation Bias Analysis in Subgroup Populations.Journal of healthcare engineering · 2022Article
- A Scoping Review of Artificial Intelligence and Machine Learning in Bariatric and Metabolic Surgery: Current Status and Future Perspectives.Obesity surgery · 2021Article
- Prediction of neonatal deaths in NICUs: development and validation of machine learning models.BMC medical informatics and decision making · 2021Article
- Review
Corrections and comments
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Authors and funding
4 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND/
objectiveOne of the most effective treatments for patients with obesity, albeit with some complications, is obesity surgery. The aim of this study was to develop a clinical decision support system (CDSS) to predict the early complications of one-anastomosis gastric bypass (OAGB) surgery. SUBJECTS/
methodsThis study was conducted in Tehran, Iran on patients who underwent OAGB surgery in 2011-2014 in five hospitals. Initially, variables affecting the OAGB early complications were identified using the literature review. Patients' data were extracted from an existing database of obesity surgery. Then, different artificial neural networks (ANNs) (multilayer perceptron (MLP) network) were developed and evaluated for prediction of 10-day, 1-month, and 3-month complications.
resultsFactors including age, BMI, smoking status, intra-operative complications, comorbidities, laboratory tests, sonography results, and endoscopy results were considered important factors for predicting early complications of OAGB. A CDSS was developed with these variables. The accuracy, specificity, and sensitivity of the 10-day prediction system in the test data were 98.4%, 98.6%, and 98.3%, respectively. These figures for 1-month system were 96%, 93%, and 98.4% and for the 3-month system were 89.3%, 86.6%, and 91.5%, respectively.
conclusionsUsing the CDSS designed, we could accurately predict the early complications of OAGB surgery.
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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.