BACKGROUND The use of machine learning(ML)to predict colonoscopy procedure duration has not been examined.AIM To assess if ML and data available at the time a colonoscopy procedure is scheduled could be used to estima...BACKGROUND The use of machine learning(ML)to predict colonoscopy procedure duration has not been examined.AIM To assess if ML and data available at the time a colonoscopy procedure is scheduled could be used to estimate procedure duration more accurately than the current practice.METHODS Total 40168 colonoscopies from the Clinical Outcomes Research Initiative database were collected.ML models predicting procedure duration were developed using data available at time of scheduling.The top performing model was compared against historical practice.Models were evaluated based on accuracy(prediction–actual time)±5,10,and 15 min.RESULTS ML outperformed historical practice with 77.1%to 68.9%,87.3%to 79.6%,and 92.1%to 86.8%accuracy at 5,10 and 15 min thresholds.CONCLUSION The use of ML to estimate colonoscopy procedure duration may lead to more accurate scheduling.展开更多
文摘BACKGROUND The use of machine learning(ML)to predict colonoscopy procedure duration has not been examined.AIM To assess if ML and data available at the time a colonoscopy procedure is scheduled could be used to estimate procedure duration more accurately than the current practice.METHODS Total 40168 colonoscopies from the Clinical Outcomes Research Initiative database were collected.ML models predicting procedure duration were developed using data available at time of scheduling.The top performing model was compared against historical practice.Models were evaluated based on accuracy(prediction–actual time)±5,10,and 15 min.RESULTS ML outperformed historical practice with 77.1%to 68.9%,87.3%to 79.6%,and 92.1%to 86.8%accuracy at 5,10 and 15 min thresholds.CONCLUSION The use of ML to estimate colonoscopy procedure duration may lead to more accurate scheduling.
