Ricciardi Carlo, Ponsiglione Alfonso Maria, Scala Arianna, Borrelli Anna, Misasi Mario, Romano Gaetano, Russo Giuseppe, Triassi Maria, Improta Giovanni
Department of Electrical Engineering and Information Technology, University of Naples "Federico II", 80125 Naples, Italy.
Department of Public Health, University Hospital of Naples "Federico II", 80131 Naples, Italy.
Bioengineering (Basel). 2022 Apr 14;9(4):172. doi: 10.3390/bioengineering9040172.
Fractures of the femur are a frequent problem in elderly people, and it has been demonstrated that treating them with a diagnostic-therapeutic-assistance path within 48 h of admission to the hospital reduces complications and shortens the length of the hospital stay (LOS). In this paper, the preoperative data of 1082 patients were used to further extend the previous research and to generate several models that are capable of predicting the overall LOS: First, the LOS, measured in days, was predicted through a regression analysis; then, it was grouped by weeks and was predicted with a classification analysis. The KNIME analytics platform was applied to divide the dataset for a hold-out cross-validation, perform a multiple linear regression and implement machine learning algorithms. The best coefficient of determination (R) was achieved by the support vector machine (R = 0.617), while the mean absolute error was similar for all the algorithms, ranging between 2.00 and 2.11 days. With regard to the classification analysis, all the algorithms surpassed 80% accuracy, and the most accurate algorithm was the radial basis function network, at 83.5%. The use of these techniques could be a valuable support tool for doctors to better manage orthopaedic departments and all their resources, which would reduce both waste and costs in the context of healthcare.
股骨骨折在老年人中是一个常见问题，并且已经证明，在患者入院48小时内通过诊断 - 治疗 - 辅助路径进行治疗可减少并发症并缩短住院时间（LOS）。在本文中，使用了1082名患者的术前数据来进一步扩展先前的研究，并生成几个能够预测总体住院时间的模型：首先，通过回归分析预测以天数衡量的住院时间；然后，按周进行分组，并通过分类分析进行预测。应用KNIME分析平台对数据集进行划分以进行留出法交叉验证，执行多元线性回归并实施机器学习算法。支持向量机获得了最佳决定系数（R）（R = 0.617），而所有算法的平均绝对误差相似，在2.00至2.11天之间。关于分类分析，所有算法的准确率都超过了80%，最准确的算法是径向基函数网络，准确率为83.5%。这些技术的应用可以成为医生更好地管理骨科科室及其所有资源的宝贵支持工具，这将在医疗保健环境中减少浪费和成本。
