基于深度信念网络的医院门诊量预测

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。

Forecasting of Hospital Outpatient Based on Deep Belief Network

Forecasting of hospital out patient plays an important role in intelligent management of modern hospital me-dical resources.The existing researches on outpatient visit analysis and forecasting mostly aim at a single set of data,lacking in in-depth analysis of the data to fully tapped.Thus a hospital outpatient prediction method based on deep belief network (DBN) was proposed.The DBN can excute unsupervised learn from hospital out-patient departments of outpatient data and complete outpatient data feature extraction for mining hidden relationships between various out patient clinic outpatient data.On top of the network layer a logistic regression is added,and the extracted data is based to forecast the future outpatient clinic outpatient.The experimental results show that prediction model based on deep learning achieves better forecasting effect of traffic outpatient capacity.