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目的 构建基于百度指数的CEEMD - GRNN模型预测HIV感染病例数、为信息缺乏的HIV感染疫情预测提供可靠的方法,旨在为艾滋病流行趋势的传统预测方法提供有益补充。方法 第一,利用GRNN建立HIV感染病例数原始序列与百度指数的非线性关系;第二,先利用CEEMD提取HIV感染病例数的周期,再利用GRNN建立提取后序列与百度指数的非线性关系;第三,基于上述两种思想进一步建立组合预测模型,称为CEEMD - GRNN组合模型;最后,将CEEMD - GRNN组合模型应用于HIV感染病例数的预测。结果 模型拟合结果表明,最优单项模型的MAPE为10.17%,CEEMD - GRNN组合模型的MAPE为7.18%,组合模型的预测精度高于最优单项模型。结论 本文提出的CEEMD - GRNN组合模型预测精度优于最优单项模型,所提模型能够为信息不充足的非线性HIV感染病例数据提供稳定可靠的预测方法。  相似文献   
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BackgroundPatients with single-ventricle physiology have a significant risk of cardiorespiratory deterioration between their first and second stage palliation surgeries.ObjectivesThe objective of this study is to develop and validate a real-time computer algorithm that can automatically recognize physiological precursors of cardiorespiratory deterioration in children with single-ventricle physiology during their interstage hospitalization.MethodsA retrospective study was conducted from prospectively collected physiological data of subjects with single-ventricle physiology. Deterioration events were defined as a cardiac arrest requiring cardiopulmonary resuscitation or an unplanned intubation. Physiological metrics were derived from the electrocardiogram (heart rate, heart rate variability, ST-segment elevation, and ST-segment variability) and the photoplethysmogram (peripheral oxygen saturation and pleth variability index). A logistic regression model was trained to separate the physiological dynamics of the pre-deterioration phase from all other data generated by study subjects. Data were split 50/50 into model training and validation sets to enable independent model validation.ResultsOur cohort consisted of 238 subjects admitted to the cardiac intensive care unit and stepdown units of Texas Children’s Hospital over a period of 6 years. Approximately 300,000 h of high-resolution physiological waveform and vital sign data were collected using the Sickbay software platform (Medical Informatics Corp., Houston, Texas). A total of 112 cardiorespiratory deterioration events were observed. Seventy-two of the subjects experienced at least 1 deterioration event. The risk index metric generated by our optimized algorithm was found to be both sensitive and specific for detecting impending events 1 to 2 h in advance of overt extremis (receiver-operating characteristic curve area: 0.958; 95% confidence interval: 0.950 to 0.965).ConclusionsOur algorithm can provide 1 to 2 h of advanced warning for 62% of all cardiorespiratory deterioration events in children with single-ventricle physiology during their interstage period, with only 1 alarm being generated at the bedside per patient per day.  相似文献   
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目的:探讨A RIM A模型在武汉市江汉区细菌性痢疾月发病率预测应用的可行性,为该区细菌性痢疾的防控提供科学参考依据。方法使用SAS9.2软件对2005~2013年该区菌痢月发病资料拟合ARIMA模型,利用建立的模型对2014年1~6月菌痢月发病率资料进行预测和效果评价。结果建立ARIMA(0,1,1)X(0,1,1)12模型拟合效果较好,预测2014年上半年疫情将呈缓慢上升趋势,预测值与实际值拟合趋势基本一致。结论 A RIM A模型可以作为该区菌痢月发病水平的短期预测模型。  相似文献   
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Prediction and control of chemical mixing are vital for many scientific areas such as subsurface reactive transport, climate modeling, combustion, epidemiology, and pharmacology. Due to the complex nature of mixing in heterogeneous and anisotropic media, the mathematical models related to this phenomenon are not analytically tractable. Numerical simulations often provide a viable route to predict chemical mixing accurately. However, contemporary modeling approaches for mixing cannot utilize available spatial-temporal data to improve the accuracy of the future prediction and can be compute-intensive, especially when the spatial domain is large and for long-term temporal predictions. To address this knowledge gap, we will present in this paper a deep learning (DL) modeling framework applied to predict the progress of chemical mixing under fast bimolecular reactions. This framework uses convolutional neural networks (CNN) for capturing spatial patterns and long short-term memory (LSTM) networks for forecasting temporal variations in mixing. By careful design of the framework—placement of non-negative constraint on the weights of the CNN and the selection of activation function, the framework ensures non-negativity of the chemical species at all spatial points and for all times. Our DL-based framework is fast, accurate, and requires minimal data for training. The time needed to obtain a forecast using the model is a fraction ($≈ \mathcal{O}(10^{−6}))$ of the time needed to obtain the result using a high-fidelity simulation. To achieve an error of 10% (measured using the infinity norm) for capturing local-scale mixing features such as interfacial mixing, only 24% to 32% of the sequence data for model training is required. To achieve the same level of accuracy for capturing global-scale mixing features, the sequence data required for model training is 64% to 70% of the total spatial-temporal data. Hence, the proposed approach—a fast and accurate way to forecast long-time spatial-temporal mixing patterns in heterogeneous and anisotropic media—will be a valuable tool for modeling reactive-transport in a wide range of applications.  相似文献   
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目的:分析fQRS与冠脉狭窄程度及主要不良心血管事件(MACE)的相关性,探讨其预测价值.方法:试验共纳入因急性心肌梗死(AMI)接受经皮冠状动脉介入(PCI)治疗的患者261例(其中心电图存在fQRS患者147例,无fQRS患者114例).分析比较患者的一般临床资料、Gensini评分,随访(14.2±0.8)月内MACE的情况.结果:(1)与无fQRS组相比,心电图存在fQRS的患者肌钙蛋白、肌酐、尿酸水平及Gensini评分较高,射血分数较低(P<0.05).(2)Kaplan-Meier生存分析提示fQRS组免于MACE的概率低于无fQRS组,Log-rank检验P<0.001.两组免于心源性死亡的生存率无显著性差异,Log-rank检验P=0.115.(3)多因素Cox回归分析显示糖尿病史、左室射血分数、心梗48 h内心电图是否存在fQRS是预测MACE的独立危险因素.结论:心梗48 h内出现fQRS是接受PCI治疗的AMI患者不良心血管事件的独立预测因子.  相似文献   
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目的:探讨X-11方法在某医院肾脏病科门诊量统计预测中的应用。方法收集某院2012—2014年各季度门诊量,利用SAS 9.1.3软件的X11过程,运用X-11方法对数据进行分析,分离季节因子,探索趋势拟合值与时间之间的关系,拟合回归方程,预测2015年一至四季度的门诊量。结果发现趋势拟合值与时间之间存在曲线关系,预测模型为Tt =15600+50001+e4.94-0.82t ,2015年一至四季度的门诊量分别为20471、21636、21329、18933。结论 X-11方法能够将季节因素分离,发现不同时间数据序列的趋势,因此预测结果值得应用。  相似文献   
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