Department of Materials and Production, Aalborg University, Fibigerstræde 16, 2-109, Aalborg Ø 9220, Danmark.
Department of Materials and Production, Aalborg University, Fibigerstræde 16, 2-115, Aalborg Ø 9220, Danmark.
Int J Med Inform. 2024 Dec;192:105631. doi: 10.1016/j.ijmedinf.2024.105631. Epub 2024 Sep 15.
In recent years, different factors such as population aging have caused escalating demand for hip and knee arthroplasty straining already limited hospitals' resources. To address this challenge, focus is put on medical and operational efficiency improvements. This includes an increased use of machine learning (ML) to predict duration of surgery (DOS) and length of stay (LOS) for total knee and total hip arthroplasty, which can be utilized for optimizing resource allocation to satisfy medical and operational limitations. This paper explores the development and performance of ML models in predicting DOS and LOS.
A systematic search of publications between 2010-2023 was conducted following PRISMA guidelines. Considering the inclusion and exclusion criteria, 28 out of 722 gathered papers from PubMed, Web of Science, and manual search were included in the study. Descriptive statistics was used to analyze the extracted data regarding data preprocessing, model development, and model performance assessment.
Most of the papers work on LOS as a binary variable. Patient's age was identified as the most frequently used and reported as important variable for predicting DOS and LOS. Investigations also illustrated that within the resulting 28 papers, more than 71% of models reached good to perfect performance based on the area under the receiver operating characteristic curve (AUC), where artificial neural networks and ensemble learning models had the biggest share among the best-performing models.
The utilization of ML models is increasing in the literature. The current performance level indicates that ML can potentially turn to powerful tools in predicting DOS and LOS for different purposes. Meanwhile, the literature is not matured yet in reporting real-life application. Future studies can focus on model specification and validation by considering empirical application.
近年来，人口老龄化等多种因素导致髋关节和膝关节置换手术的需求不断增加，给本来就有限的医院资源带来了更大的压力。为了应对这一挑战，人们关注的重点是提高医疗和运营效率。这包括更多地使用机器学习（ML）来预测全膝关节和全髋关节置换术的手术持续时间（DOS）和住院时间（LOS），从而可以优化资源分配，以满足医疗和运营方面的限制。本文探讨了 ML 模型在预测 DOS 和 LOS 方面的发展和性能。
根据 PRISMA 指南，对 2010 年至 2023 年间的出版物进行了系统检索。考虑到纳入和排除标准，从 PubMed、Web of Science 和手动搜索中收集了 722 篇论文，其中 28 篇符合研究要求。采用描述性统计方法分析了关于数据预处理、模型开发和模型性能评估的数据。
大多数论文都将 LOS 作为二分类变量进行研究。患者年龄被确定为预测 DOS 和 LOS 最常用和最重要的变量。研究还表明，在所研究的 28 篇论文中，超过 71%的模型基于接受者操作特征曲线（ROC）下的面积（AUC）达到了良好到完美的性能，其中人工神经网络和集成学习模型在表现最好的模型中占比最大。
文献中越来越多地使用 ML 模型。目前的性能水平表明，ML 有可能成为预测不同目的 DOS 和 LOS 的强大工具。同时，文献在报告实际应用方面还不够成熟。未来的研究可以关注模型的规范和验证，考虑实证应用。
