PDF(Pubmed)
Abstract：
BACKGROUND: Accurate projections of procedural case durations are complex but critical to the planning of perioperative staffing, operating room resources, and patient communication. Nonlinear prediction models using machine learning methods may provide opportunities for hospitals to improve upon current estimates of procedure duration.
OBJECTIVE: The aim of this study was to determine whether a machine learning algorithm scalable across multiple centers could make estimations of case duration within a tolerance limit because there are substantial resources required for operating room functioning that relate to case duration.
METHODS: Deep learning, gradient boosting, and ensemble machine learning models were generated using perioperative data available at 3 distinct time points: the time of scheduling, the time of patient arrival to the operating or procedure room (primary model), and the time of surgical incision or procedure start. The primary outcome was procedure duration, defined by the time between the arrival and the departure of the patient from the procedure room. Model performance was assessed by mean absolute error (MAE), the proportion of predictions falling within 20% of the actual duration, and other standard metrics. Performance was compared with a baseline method of historical means within a linear regression model. Model features driving predictions were assessed using Shapley additive explanations values and permutation feature importance.
RESULTS: A total of 1,177,893 procedures from 13 academic and private hospitals between 2016 and 2019 were used. Across all procedures, the median procedure duration was 94 (IQR 50-167) minutes. In estimating the procedure duration, the gradient boosting machine was the best-performing model, demonstrating an MAE of 34 (SD 47) minutes, with 46% of the predictions falling within 20% of the actual duration in the test data set. This represented a statistically and clinically significant improvement in predictions compared with a baseline linear regression model (MAE 43 min; P<.001; 39% of the predictions falling within 20% of the actual duration). The most important features in model training were historical procedure duration by surgeon, the word \"free\" within the procedure text, and the time of day.
CONCLUSIONS: Nonlinear models using machine learning techniques may be used to generate high-performing, automatable, explainable, and scalable prediction models for procedure duration.
OBJECTIVE: The aim of this study was to determine whether a machine learning algorithm scalable across multiple centers could make estimations of case duration within a tolerance limit because there are substantial resources required for operating room functioning that relate to case duration.
METHODS: Deep learning, gradient boosting, and ensemble machine learning models were generated using perioperative data available at 3 distinct time points: the time of scheduling, the time of patient arrival to the operating or procedure room (primary model), and the time of surgical incision or procedure start. The primary outcome was procedure duration, defined by the time between the arrival and the departure of the patient from the procedure room. Model performance was assessed by mean absolute error (MAE), the proportion of predictions falling within 20% of the actual duration, and other standard metrics. Performance was compared with a baseline method of historical means within a linear regression model. Model features driving predictions were assessed using Shapley additive explanations values and permutation feature importance.
RESULTS: A total of 1,177,893 procedures from 13 academic and private hospitals between 2016 and 2019 were used. Across all procedures, the median procedure duration was 94 (IQR 50-167) minutes. In estimating the procedure duration, the gradient boosting machine was the best-performing model, demonstrating an MAE of 34 (SD 47) minutes, with 46% of the predictions falling within 20% of the actual duration in the test data set. This represented a statistically and clinically significant improvement in predictions compared with a baseline linear regression model (MAE 43 min; P<.001; 39% of the predictions falling within 20% of the actual duration). The most important features in model training were historical procedure duration by surgeon, the word \"free\" within the procedure text, and the time of day.
CONCLUSIONS: Nonlinear models using machine learning techniques may be used to generate high-performing, automatable, explainable, and scalable prediction models for procedure duration.
摘要：
背景：手术病例持续时间的准确预测是复杂的，但对于围手术期人员配置的规划至关重要。手术室资源,和病人沟通。使用机器学习方法的非线性预测模型可以为医院提供改进对程序持续时间的当前估计的机会。
目的：这项研究的目的是确定跨多个中心可扩展的机器学习算法是否可以在容差范围内对病例持续时间进行估计，因为手术室功能需要大量的资源与病例持续时间有关。
方法：深度学习，梯度增强，并使用3个不同时间点的围手术期数据生成集成机器学习模型：调度时间，患者到达手术室或手术室的时间（主要模型），以及手术切口或手术开始的时间。主要结果是手术持续时间，由患者到达和离开手术室之间的时间定义。通过平均绝对误差(MAE)评估模型性能，预测比例在实际持续时间的20%以内，和其他标准指标。在线性回归模型中，将性能与历史均值的基线方法进行了比较。使用Shapley加性解释值和置换特征重要性评估驱动预测的模型特征。
结果：在2016年至2019年期间，共使用了13家学术和私立医院的1,177,893例手术。在所有程序中，中位手术持续时间为94(IQR50-167)分钟.在估计过程持续时间时，梯度增压机是性能最好的型号，表现出34(SD47)分钟的MAE，46%的预测落在测试数据集中实际持续时间的20%以内。与基线线性回归模型相比，这代表了预测的统计学和临床显着改善（MAE43分钟；P<.001；39％的预测落在实际持续时间的20％之内）。模型训练中最重要的特征是外科医生的历史手术持续时间，程序文本中的“免费”一词，和一天中的时间。
结论：使用机器学习技术的非线性模型可用于生成高性能，可自动化，可以解释，和可扩展的手术持续时间预测模型。
目的：这项研究的目的是确定跨多个中心可扩展的机器学习算法是否可以在容差范围内对病例持续时间进行估计，因为手术室功能需要大量的资源与病例持续时间有关。
方法：深度学习，梯度增强，并使用3个不同时间点的围手术期数据生成集成机器学习模型：调度时间，患者到达手术室或手术室的时间（主要模型），以及手术切口或手术开始的时间。主要结果是手术持续时间，由患者到达和离开手术室之间的时间定义。通过平均绝对误差(MAE)评估模型性能，预测比例在实际持续时间的20%以内，和其他标准指标。在线性回归模型中，将性能与历史均值的基线方法进行了比较。使用Shapley加性解释值和置换特征重要性评估驱动预测的模型特征。
结果：在2016年至2019年期间，共使用了13家学术和私立医院的1,177,893例手术。在所有程序中，中位手术持续时间为94(IQR50-167)分钟.在估计过程持续时间时，梯度增压机是性能最好的型号，表现出34(SD47)分钟的MAE，46%的预测落在测试数据集中实际持续时间的20%以内。与基线线性回归模型相比，这代表了预测的统计学和临床显着改善（MAE43分钟；P<.001；39％的预测落在实际持续时间的20％之内）。模型训练中最重要的特征是外科医生的历史手术持续时间，程序文本中的“免费”一词，和一天中的时间。
结论：使用机器学习技术的非线性模型可用于生成高性能，可自动化，可以解释，和可扩展的手术持续时间预测模型。
