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通过数据挖掘和预测建模进行手术时长估计:一个案例研究

Surgical Duration Estimation via Data Mining and Predictive Modeling: A Case Study.

作者信息

Hosseini N, Sir M Y, Jankowski C J, Pasupathy K S

机构信息

Health Care Policy & Research, Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic.

Department of Anesthesiology, Mayo Clinic.

出版信息

AMIA Annu Symp Proc. 2015 Nov 5;2015:640-8. eCollection 2015.

Abstract

Operating rooms (ORs) are one of the most expensive and profitable resources within a hospital system. OR managers strive to utilize these resources in the best possible manner. Traditionally, surgery durations are estimated using a moving average adjusted by the scheduler (adjusted system prediction or ASP). Other methods based on distributions, regression and data mining have also been proposed. To overcome difficulties with numerous procedure types and lack of sufficient sample size, and avoid distributional assumptions, the main objective is to develop a hybrid method of duration prediction and demonstrate using a case study.

摘要

手术室是医院系统中最昂贵且盈利的资源之一。手术室管理人员努力以最佳方式利用这些资源。传统上,手术时长是通过调度员调整后的移动平均值(调整后的系统预测或ASP)来估计的。还提出了基于分布、回归和数据挖掘的其他方法。为克服多种手术类型带来的困难以及样本量不足的问题,并避免分布假设,主要目标是开发一种时长预测的混合方法并通过案例研究进行演示。

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