Key Experimental Factors of Machine Learning-Based Identification of Surgery Cancellations

Abstract

This study aimed to provide effective methods for the identification of surgeries with high cancellation risk based on machine learning models and analyze the key factors that affect the identification performance. The data covered the period from January 1, 2013, to December 31, 2014, at West China Hospital in China, which focus on elective urologic surgeries. All surgeries were scheduled one day in advance, and all cancellations were of institutional resource- and capacity-related types. Feature selection strategies, machine learning models, and sampling methods are the most discussed topic in general machine learning researches and have a direct impact on the performance of machine learning models. Hence, they were considered to systematically generate complete schemes in machine learning-based identification of surgery cancellations. The results proved the feasibility and robustness of identifying surgeries with high cancellation risk, with the considerable maximum of area under the curve (AUC) (0.7199) for random forest model with original sampling using backward selection strategy. In addition, one-side Delong test and sum of square error analysis were conducted to measure the effects of feature selection strategy, machine learning model, and sampling method on the identification of surgeries with high cancellation risk, and the selection of machine learning model was identified as the key factors that affect the identification of surgeries with high cancellation risk. This study offers methodology and insights for identifying the key experimental factors for identifying surgery cancellations, and it is helpful to further research on machine learning-based identification of surgeries with high cancellation risk.

Artificial Intelligence in Operating Room Management

“…After screening the remaining 30 studies, we discarded eight papers: two were not strictly related to ML application, and six were theoretical studies. In the final selection, 22 studies were included in the analysis [ 8 – 29 ]. Figure 1 displays the PRISMA flowchart.…”

“…Among the 22 studies analyzed [ 8 – 29 ], sixteen primarily focused on predicting the duration of surgical cases [ 8 – 13 , 15 – 24 ], three centered on predicting the length of stay in the PACU [ 25 – 27 ]. One study addressed both aspects [ 14 ], while only two studies examined the identification of surgical cases at high risk of cancellation [ 28 , 29 ].…”

Optimization Scheme of Accurate Calculation Algorithm of Electric Charge Based on Machine Learning

“…In the accurate accounting, the traditional accurate payment algorithm of electricity bills can no longer meet the current actual needs, so it is necessary to find a more efficient, simple and effective method [1][2]. At present, it is commonly used to propose a lot of targeted and low cost electric charge accurate accounting schemes based on machine learning, including multiple design ideas such as the combination of multi-dimensional coding method and moment allocation strategy [3][4]. Two new development tools, artificial neural network technology and deep learning algorithm, are introduced in the implementation process, and MATLAB software is used for simulation experiment analysis and verification [5].…”

Optimizing Operation Room Utilization—A Prediction Model

“…However, while adopting artificial intelligence (AI) in imaging focuses on analyzing a constellation of unstructured data (volume), implementing AI in operating room planning strategy differs slightly from our model, which is based on multi-dimensional data that change over time (variety, velocity, and volume). Recently, ML models for OR facilities were developed: for example, to analyze the key factors that affect the identification of surgeries with high cancellation risk [20]; to improve the accuracy of duration prediction of complex surgical activities, such as the duration of robot-assisted surgery [21]; and to improve the whole surgical workflow [22]. ML can also be used for improving surgery training [23].…”