Jahandideh Sepideh, Jahandideh Samad, Asadabadi Ebrahim Barzegari, Askarian Mehrdad, Movahedi Mohammad Mehdi, Hosseini Somayyeh, Jahandideh Mina
Department of Hospital Management, Shiraz University of Medical Sciences, Shiraz, Iran.
Waste Manag. 2009 Nov;29(11):2874-9. doi: 10.1016/j.wasman.2009.06.027. Epub 2009 Jul 29.
Prediction of the amount of hospital waste production will be helpful in the storage, transportation and disposal of hospital waste management. Based on this fact, two predictor models including artificial neural networks (ANNs) and multiple linear regression (MLR) were applied to predict the rate of medical waste generation totally and in different types of sharp, infectious and general. In this study, a 5-fold cross-validation procedure on a database containing total of 50 hospitals of Fars province (Iran) were used to verify the performance of the models. Three performance measures including MAR, RMSE and R(2) were used to evaluate performance of models. The MLR as a conventional model obtained poor prediction performance measure values. However, MLR distinguished hospital capacity and bed occupancy as more significant parameters. On the other hand, ANNs as a more powerful model, which has not been introduced in predicting rate of medical waste generation, showed high performance measure values, especially 0.99 value of R(2) confirming the good fit of the data. Such satisfactory results could be attributed to the non-linear nature of ANNs in problem solving which provides the opportunity for relating independent variables to dependent ones non-linearly. In conclusion, the obtained results showed that our ANN-based model approach is very promising and may play a useful role in developing a better cost-effective strategy for waste management in future.
预测医院废物产生量将有助于医院废物管理的储存、运输和处置。基于这一事实，应用了包括人工神经网络（ANNs）和多元线性回归（MLR）在内的两种预测模型，来预测医疗废物的总产生率以及不同类型的锐器、传染性和一般性医疗废物的产生率。在本研究中，对一个包含伊朗法尔斯省总共50家医院的数据库进行了5折交叉验证程序，以验证模型的性能。使用包括平均绝对误差（MAR）、均方根误差（RMSE）和决定系数（R(2)）在内的三个性能指标来评估模型的性能。作为传统模型的MLR获得了较差的预测性能指标值。然而，MLR将医院容量和床位占用率识别为更显著的参数。另一方面，人工神经网络作为一种更强大的模型，在预测医疗废物产生率方面尚未被引入，其表现出较高的性能指标值，尤其是R(2)值为0.99，证实了数据的良好拟合。如此令人满意的结果可归因于人工神经网络在解决问题时的非线性性质，它为将自变量与因变量进行非线性关联提供了机会。总之，所得结果表明，我们基于人工神经网络的模型方法非常有前景，可能在未来制定更好的具有成本效益的废物管理策略中发挥有益作用。
