标题： 使用神经异方差回归预测手术时长 显示英文标题

标题： Predicting Surgery Duration with Neural Heteroscedastic Regression

摘要： 手术排程是一项具有挑战性的任务，这是由于临床环境的基本不确定性，以及与预订不足和过度预订相关的风险和成本。 我们研究了神经回归算法来估计手术病例的持续时间参数，重点关注异方差性问题。 我们旨在同时估计每台手术的持续时间，以及针对每台手术的我们对其持续时间的不确定性概念。 估计这种不确定性可以带来更细致和有效的排程策略，因为我们能够更高效地安排手术，同时允许有根据的和特定于案例的误差范围。 使用来自加州大学圣地亚哥健康系统的手术记录，从一个大型美国医疗系统中，我们展示了相对于当前排程技术，在过度预订分钟数方面有可能提高20%左右。 此外，我们证明了手术持续时间确实存在异方差性。 我们表明，能够估计特定病例不确定性的模型更好地拟合数据（对数似然）。 此外，我们表明异方差预测可以在过度预订和预订不足分钟数之间更优地权衡，尤其是在空闲分钟数和排程冲突带来的成本差异较大时。 显示英文摘要

摘要： Scheduling surgeries is a challenging task due to the fundamental uncertainty of the clinical environment, as well as the risks and costs associated with under- and over-booking. We investigate neural regression algorithms to estimate the parameters of surgery case durations, focusing on the issue of heteroscedasticity. We seek to simultaneously estimate the duration of each surgery, as well as a surgery-specific notion of our uncertainty about its duration. Estimating this uncertainty can lead to more nuanced and effective scheduling strategies, as we are able to schedule surgeries more efficiently while allowing an informed and case-specific margin of error. Using surgery records %from the UC San Diego Health System, from a large United States health system we demonstrate potential improvements on the order of 20% (in terms of minutes overbooked) compared to current scheduling techniques. Moreover, we demonstrate that surgery durations are indeed heteroscedastic. We show that models that estimate case-specific uncertainty better fit the data (log likelihood). Additionally, we show that the heteroscedastic predictions can more optimally trade off between over and under-booking minutes, especially when idle minutes and scheduling collisions confer disparate costs.