计算机科学 ›› 2016, Vol. 43 ›› Issue (Z11): 26-30.doi: 10.11896/j.issn.1002-137X.2016.11A.006
杨旭华,钟楠祎
YANG Xu-hua and ZHONG Nan-yi
摘要： 有效的医院门诊量预测是现代医院对医疗资源实现智能化管理的重要前提之一。现有的医院门诊量预测方法大多针对的是单一的数据集,缺少对数据的充分挖掘和深入分析。为此,提出一种基于深度信念网络的医院门诊量预测方法,用深度信念网络对医院各科室的门诊量数据进行无监督学习,完成对门诊量数据的特征提取,挖掘各科室门诊量数据间的相互关系,在网络的顶层叠加一个逻辑回归层并将提取出的数据特征作为输入来预测各科室未来的门诊量。仿真实验结果表明,基于深度学习的预测模型可以得到较高的门诊量预测精度,是一种可行且有效的预测方法。
| [1] Valipour M,Banihabib M E,Behbahani S M R.Comparison of the ARMA,ARIMA,and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir[J].Journal of Hydrology,2013,476(2):433-441 [2] Khashei M,Bijari M.A novel hybridization of artificial neural networks and ARIMA models for time series forecasting[J].Applied Soft Computing,2011,11(2):2664-2675 [3] Wang Y,Dang Y,Li Y,et al.An approach to increase prediction precision of GM(1,1) model based on optimization of the initial condition[M]∥Continuous flows in the plane.Springer,1974,37(8):5640-5644 [4] Yin Z Y.Study on the fitting methods of logistic curve[J].Application of Statistics and Management,2002,21(1):41-46 [5] 郭丽丽,丁世飞.深度学习研究进展[J].计算机科学,2015,42(5):28-33 [6] LeCun Y,Bengio Y,Hinton G.Deep learning[J].Nature,2015,521(7553):436-444 [7] Bengio Y.Learning Deep Architectures for AI[J].Foundations &Trends in Machine Learning,2009,2(1):1-127 [8] Hinton G E,Osindero S,Teh Y W.A Fast Learning Algorithm for Deep Belief Nets[J].Neural Computation,1960,18(7):1527-1554 [9] Leung M K,Xiong H Y,Lee L J,et al.Deep learning of the tissue-regulated splicing code[J].Bioinformatics,2014,30(12):i121-i129 [10] Xiong H Y,et al.RNA Splicing,The human splicing code re-veals new insights into the genetic determinants of disease[J].Science,2015,347(6218) [11] Collobert R,et al.Natural language processing (almost) from scratch[J].Journal of Machine Learning Research,2011,12(1):2493-2537 [12] Sutskever I,Vinyals O,Le Q V.Sequence to sequence learning with neural networks[C]∥Proc.Advances in Neural Information Processing Systems.2014(27):3104-3112 [13] Bordes A,Chopra S,Weston J.Question Answering with Subgraph Embeddings[J].Computer Science,2014 [14] Jean S,Cho K,Memisevic R,et al.On using very large target vocabulary for neural machine translation[C]∥Proc.ACL-IJCNLP,2015 [15] Etemad-Shahidi A,Yasa R,Kazeminezhad M H.Prediction ofwave-induced scour depth under submarine pipelines using machine learning approach[J].Applied Ocean Research,2011,33(1):54-59 [16] Lv Y,Duan Y,Kang W,et al.Traffic Flow Prediction With Big Data:A Deep Learning Approach[J].IEEE Transactions on Intelligent Transportation Systems,2015,16(2):1-9 [17] Hu Q,Zhang R,Zhou Y.Transfer learning for short-term wind speed prediction with deep neural networks[J].Renewable Energy,2016,85:83-95 [18] 张艳红,王宝会.基于深度神经网络的社会媒体网络分析[J].计算机科学,2016,43(4):252-255 [19] Hossain M,Rekabdar B,Louis S J,et al.Forecasting the weather of Nevada:A deep learning approach[C]∥2015 International Joint Conference on Neural Networks(IJCNN).2015 [20] 耿娟.ARIMA模型在医院门诊量预测中的应用[J].中国卫生统计,2014(4):643-645 [21] 叶明全,胡学钢.基于季节性神经网络的医院门诊量曲线拟合与预测[J].图学学报,2005,26(2):83-86 [22] 朱顺痣,王大寒,何亚男,等.基于时间序列模型的医院门诊量分析与预测[J].中国科学技术大学学报,2015,45(10):795-803 [23] 张筠莉,杨祯山.现代医院门诊量的灰色RBF神经网络预测[J].计算机工程与应用,2010,6(29):225-228 |
| No related articles found! |
|
||
首页