ArticleJournal of medical systems2025
Development of Predictive Model of Surgical Case Durations Using Machine Learning Approach.
Article in Journal of medical systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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
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
8 citing papers in PubMed.
- Accurate surgery time prediction (ASTP) strategy based on artificial intelligence techniques.Scientific reports · 2026Article
- An Emerging Role of Artificial Intelligence in Pediatric Neuroanesthesia.Journal of Korean Neurosurgical Society · 2026Article
- Predicting Pediatric Urological Surgery Duration Through Multimodal Patient-Physician Feature Fusion: Deep Learning Framework Incorporating Clinical Text Embedding.JMIR medical informatics · 2026Article
- Machine Learning-Driven Prediction of Intensive Care Units Mortality and Length of Stay: A 11-Year Retrospective Study in Hong Kong Public Hospitals.Journal of medical systems · 2026Article
- Development and Evaluation of an Operative Case Length Prediction Model in Adult Surgical Patients.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2026Article
- Development and validation of machine learning models for predicting operative duration in assisted reproductive technology procedures.BMC medical research methodology · 2026Article
- Development and clinical application of an Intelligent Operating Room Scheduling System.Frontiers in medicine · 2026Article
- Innovative Technologies for Smarter and Efficient Operating Room Scheduling.Journal of medical systems · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Optimizing operating room (OR) utilization is critical for enhancing hospital management and operational efficiency. Accurate surgical case duration predictions are essential for achieving this optimization. Our study aimed to refine the accuracy of these predictions beyond traditional estimation methods by developing Random Forest models tailored to specific surgical departments. Utilizing a comprehensive dataset, we applied several machine learning algorithms, including RandomForest, XGBoost, Linear Regression, LightGBM, and CatBoost, and assessed their performance using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-Squared (R
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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.