Probabilistic forecasting of surgical case duration using machine learning: model development and validation
Probabilistic forecasting of surgical case duration using machine learning: model development and validation
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DOI：
10.1093/jamia/ocaa140
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发表时间：
2020-12-01
影响因子：
6.4
通讯作者：
Kannampallil, Thomas
中科院分区：
文献类型：
--
作者：
Jiao, York;Sharma, Anshuman;Kannampallil, Thomas
目的：准确估计手术时间可以提高手术室的成本效益。我们开发了一种新的机器学习方法，使用结构化和非结构化的功能作为输入，预测连续的概率分布的手术casesduals.Materials和方法：数据集包括53 783例手术病例进行了4年多的tertiarycare儿科医院。提取的特征包括分类（美国麻醉医师协会[阿萨]身体状况、住院状态、星期几）、连续（计划手术持续时间、患者年龄）和非结构化文本（手术名称、手术诊断）变量。混合密度网络（MDN）进行了训练，并比较了多个基于树的方法和贝叶斯统计方法。一个连续的排名概率得分（CRPS），平均绝对误差的广义扩展，是主要的性能指标。计算弹球损失（PL）以评估特定分位数的准确度。对常见和罕见外科手术的性能指标进行了额外评价。结果：MDN具有最好的性能，CRPS为18.1分钟，相比基于树的方法（19.5-22.1分钟）和贝叶斯方法（21.2分钟）。MDN在所有分位数下的PL最好，常见和罕见手术的CRPS和PL最好。预定的持续时间和程序名称是最重要的功能，在MDN.Conclusions：使用自然语言处理的手术描述符，我们证明了使用ML方法来预测连续概率分布的手术时间。基于ML的MDN方法的更敏锐的预测为指导智能计划设计和手术日操作决策提供了机会。
Objective: Accurate estimations of surgical case durations can lead to the cost-effective utilization of operating rooms. We developed a novel machine learning approach, using both structured and unstructured features as input, to predict a continuous probability distribution of surgical case durations.Materials and Methods: The data set consisted of 53 783 surgical cases performed over 4 years at a tertiarycare pediatric hospital. Features extracted included categorical (American Society of Anesthesiologists [ASA] Physical Status, inpatient status, day of week), continuous (scheduled surgery duration, patient age), and unstructured text (procedure name, surgical diagnosis) variables. A mixture density network (MDN) was trained and compared to multiple tree-based methods and a Bayesian statistical method. A continuous ranked probability score (CRPS), a generalized extension of mean absolute error, was the primary performance measure. Pinball loss (PL) was calculated to assess accuracy at specific quantiles. Performance measures were additionally evaluated on common and rare surgical procedures. Permutation feature importance was measured for the best performing model.Results: MDN had the best performance, with a CRPS of 18.1 minutes, compared to tree-based methods (19.5-22.1 minutes) and the Bayesian method (21.2 minutes). MDN had the best PL at all quantiles, and the best CRPS and PL for both common and rare procedures. Scheduled duration and procedure name were the most important features in the MDN.Conclusions: Using natural language processing of surgical descriptors, we demonstrated the use of ML approaches to predict the continuous probability distribution of surgical case durations. The more discerning forecast of the ML-based MDN approach affords opportunities for guiding intelligent schedule design and dayof-surgery operational decisions.