本文關(guān)鍵詞： 深度信念網(wǎng)絡(luò) 門診量預(yù)測 數(shù)據(jù)特征 邏輯回歸　出處：《計算機科學(xué)》2016年S2期 　論文類型：期刊論文

【摘要】：有效的醫(yī)院門診量預(yù)測是現(xiàn)代醫(yī)院對醫(yī)療資源實現(xiàn)智能化管理的重要前提之一�，F(xiàn)有的醫(yī)院門診量預(yù)測方法大多針對的是單一的數(shù)據(jù)集,缺少對數(shù)據(jù)的充分挖掘和深入分析。為此,提出一種基于深度信念網(wǎng)絡(luò)的醫(yī)院門診量預(yù)測方法,用深度信念網(wǎng)絡(luò)對醫(yī)院各科室的門診量數(shù)據(jù)進(jìn)行無監(jiān)督學(xué)習(xí),完成對門診量數(shù)據(jù)的特征提取,挖掘各科室門診量數(shù)據(jù)間的相互關(guān)系,在網(wǎng)絡(luò)的頂層疊加一個邏輯回歸層并將提取出的數(shù)據(jù)特征作為輸入來預(yù)測各科室未來的門診量。仿真實驗結(jié)果表明,基于深度學(xué)習(xí)的預(yù)測模型可以得到較高的門診量預(yù)測精度,是一種可行且有效的預(yù)測方法。
[Abstract]:Effective outpatient volume prediction is one of the important premises for modern hospital to realize intelligent management of medical resources. Most of the existing methods of outpatient volume prediction are aimed at a single data set. For this reason, a method of outpatient quantity prediction based on deep belief network is proposed, which can be used to study the outpatient data of each department without supervision. Complete the feature extraction of the outpatient data, mining the relationship between the outpatient data of each department, A logical regression layer is superimposed on the top of the network, and the extracted data feature is used as input to predict the future outpatient volume of each department. The simulation results show that the prediction model based on in-depth learning can obtain higher prediction accuracy of outpatient volume. It is a feasible and effective prediction method.
【作者單位】： 浙江工業(yè)大學(xué)計算機科學(xué)與技術(shù)學(xué)院;
【基金】：國家自然科學(xué)基金(61374152)資助
【分類號】：R197.3;TP183
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本文編號：1509183