ArticleScientific reports2024
Machine learning analysis of lab tests to predict bariatric readmissions.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Insights from deep learning models on new-onset anxiety in patients following bariatric metabolic surgery.Journal of affective disorders reports · 2026Article
- Development and Internal Validation of a Multivariable Prediction Model for Postoperative Bleeding in Patients Undergoing Bariatric Surgery (The POD 1-DISCHARGE Calculator).Obesity surgery · 2026Article
- Development and External Validation of a Machine Learning-Based Risk Score for Stent Outcomes in Post-Bariatric Leak Management: The "Alexandria-Bari-Stent" Tool.Obesity surgery · 2025Article
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Authors and funding
5 authors.
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Abstract
The purpose of this study was to develop a machine learning model for predicting 30-day readmission after bariatric surgery based on laboratory tests. Data were collected from patients who underwent bariatric surgery between 2018 and 2023. Laboratory test indicators from the preoperative stage, one day postoperatively, and three days postoperatively were analyzed. Least absolute shrinkage and selection operator regression was used to select the most relevant features. Models constructed included support vector machine (SVM), generalized linear model, multi-layer perceptron, random forest, and extreme gradient boosting. Model performance was evaluated and compared using the area under the receiver operating characteristic curve (AUROC). A total of 1262 patients were included, of which 7.69% of cases were readmitted. The SVM model achieved the highest AUROC (0.784; 95% CI 0.696-0.872), outperforming other models. This suggests that machine learning models based on laboratory test data can effectively identify patients at high risk of readmission after bariatric surgery.
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Registered trials
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