Background: When kids stay in the hospital for a long time after having their tonsils or adenoids removed, it costs more money and can affect their care. It’s important to understand why some kids stay longer than others.
Goal: This study used computer programs (machine learning) to predict how long kids will stay in the hospital after surgery and to find out what factors affect their stay.
How they did it: They looked at information from 423 children who had tonsil or adenoid surgery in a hospital in Italy. They collected details about the kids, their surgery, and what happened after. They used special computer programs to analyze this information.
What they found: Things that made kids stay longer included:
- Feeling sick or throwing up after surgery
- Using certain medicines during surgery
- Having pain when leaving the hospital
- How long it took to remove the breathing tube after surgery
The best computer program they used was very good at predicting how long kids would stay in the hospital.
Conclusion: These computer programs can help doctors predict how long kids will need to stay in the hospital and understand why some stay longer. This information can help improve care for kids after surgery and help hospitals use their resources better. In the future, doctors might use these programs to make decisions while treating patients.
Keywords: Pediatric surgery, tonsillectomy, adenoidectomy, machine learning, artificial intelligence, hospital discharge, length of stay, postoperative complications, anesthesia, fentanyl, dexmedetomidine, postoperative nausea and vomiting, pain management, extubation time, predictive modeling, AdaBoost, random forest, logistic regression, RUSBoost, feature importance, healthcare efficiency, patient outcomes, perioperative care, ambulatory surgery, pediatric anesthesia, opioid-free anesthesia, recovery room, post-anesthesia care unit, emergence delirium, risk factors, hospital readmission, healthcare costs, clinical decision support, electronic health records, data preprocessing, cross-validation, confusion matrix, ROC curve, precision-recall curve, F1 score, classification algorithms, ensemble learning, imbalanced datasets, synthetic minority over-sampling technique, clinical workflow integration, patient safety, resource allocation, healthcare quality improvement, personalized medicine, pediatric pain assessment, postoperative monitoring.